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$21 USD / jam
Bendera INDIA
amethi, india
$21 USD / jam
Sekarang jam 5:19 PTG di sini
Menyertai Disember 12, 2018
3 Cadangan

Aditya Vikram S.

@singhaditya5842

annual-level-three.svg 5.0 (9 ulasan)
6.2
6.2
100%
100%
$21 USD / jam
Bendera INDIA
amethi, india
$21 USD / jam
100%
Pekerjaan Disiapkan
100%
Mengikut Bajet
100%
Tepat Pada Masa
12%
Kadar Upah Semula

Data Scientist || Machine Learning Engineer

As an AI/ML professional with over 5+ years of experience, I possess a strong skillset in natural language processing (NLP), computer vision (CV), and recommendation systems. My expertise in these areas is best exemplified by my work on Smart Care Plan and Automatic Number Plate Recognition (ANPR), two highly impactful projects that required extensive knowledge of NLP, CV, and recommendation systems. I have experience in deploying the model in low-powered devices and in AWS services. Technical Expertise: Languages: Python, SQL, JAVA, C, C++, Rust, MATLAB Libraries: Tensorflow, PyTorch, OpenCV, Numpy, Pandas, Scikit-Learn, SciPy, NLTL, SpaCy, Xgboost, Matplotlib, Seaborn, PyQT5, etc. Framework: FastAPI, Flask, Pydantic, SQLAlchamy ORMs Database: Postgres, Snowflake, MySQL, MS Server, MongoDB Tools: Kafka, DAGs, DBT, IntelliJ, VS Code, OpenAI Technologies: AWS EC2, S3-bucket, AWS Sagemaker, AWS Lambda Function, GCP, etc. Others: Machine learning, deep learning, computer vision, natural language processing, data analysis, supervised learning, unsupervised learning, recommendation system
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Portfolio

8623829
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
8591084
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
7254901
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
7204132
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
7204103
Extracted the address, contact number, rating, and the number-of-reviews for all warehouses of TVs Logistics in India. Note: This extracts all those warehouses which are available on google.co.in/search
TVs Logistics Warehouses Data from Google Map
Extracted the address, contact number, rating, and the number-of-reviews for all warehouses of TVs Logistics in India. Note: This extracts all those warehouses which are available on google.co.in/search
TVs Logistics Warehouses Data from Google Map
7204097
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN
8623829
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
I built an ANPR system from scratch using advanced algorithms. This system has a 95.3% hit-rate and it works for both kinds of license plates (Linear and bilinear). This system is specially designed for Europian Countries. This system works for almost all special characters of Europian countries like (Ä, Ö, Ü, Č, etc.) I followed the following steps to complete this project: 1. Data collection for license plate detection. Used open-source labeled data here and trained deep-learning model for this task. 2. Collected data for the OCR part. There is not enough labeled data for this task. So manually labeling of the collected-data was done. Then trained the OCR model (CRAFT and CRNN). 3. To improve the performance on inclined license plates, one more model was trained. Note: This is a very lengthy project, I can not share all details here.
Automatic License Plate Recognition (ANPR) System
8591084
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
It is a data visualization project where I plotted various plots from the given data. The data of this project was time-series data. The starting date of data is Timestamp('2015-10-04 19:08:10') and the ending date of data is Timestamp('2015-10-16 13:09:53'). Process: * Read the description of data thoroughly to understand the data * Did a lot of cleaning before plotting * Then did the plotting. I made 9 plots and one map plot where you can visualize the planes in a region.
Data Visualization of flight data
7254901
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
Made a python script that can scrap all data from all pages for the given keywords (like `restaurant`, `barber shop`, etc.) and given locations (like `New York`, `Manhattan`, etc). Scrapped data is stored in CSV format. With the help of this script, I can scrap the data from the YellowPage website for any keyword and any location.
Scrapping the YellowPage
7204132
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
First Extracted CIN, Name, Address, and link of companies. The link contains more details about the company and that information is also extracted. After extracting and cleaning all those details, data is put in the Sqlte3 database.
Etract the Company Details From Zubagroup.com
7204103
Extracted the address, contact number, rating, and the number-of-reviews for all warehouses of TVs Logistics in India. Note: This extracts all those warehouses which are available on google.co.in/search
TVs Logistics Warehouses Data from Google Map
Extracted the address, contact number, rating, and the number-of-reviews for all warehouses of TVs Logistics in India. Note: This extracts all those warehouses which are available on google.co.in/search
TVs Logistics Warehouses Data from Google Map
7204097
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN
Extracted the posts that are relevant to some tags like `#warehouse #requirement`, `#logistics #requirement` from LinkedIn, and then cleaned the data as per requirement. Selenium was used to extract the data.
Warehouse Details Extraction from LinkedIN

Ulasan

Perubahan disimpan
Menunjukkan 1 - 5 daripada 9 ulasan
 Tapis ulasan mengikut:
5.0
€6,218.96 EUR
Project for Aditya Vikram S. -- 3
Great work, gladly again!
Python
Web Scraping
Natural Language
OpenCV
+1 lagi
J
Bendera Jan-Christopher W.
@jwoehrl
•
3 tahun yang lalu
5.0
€25.00 EUR
Project for Aditya Vikram S.
Aditya answered me many questions about deep learning and supported me to realize bugs in my code. I 100% recommand him to you because his service is awsome and efficient! You should give it a try!
Python
Web Scraping
Natural Language
OpenCV
+1 lagi
L
Closed User
@LinusKao
•
3 tahun yang lalu
5.0
€4,428.93 EUR
Project for Aditya Vikram S.
Excellent freelancer. Very good machine learning engineer. Work in the field of image recognition was carried out for us. It was delivered on time and to our complete satisfaction.
Python
Web Scraping
Natural Language
OpenCV
+1 lagi
J
Bendera Jan-Christopher W.
@jwoehrl
•
3 tahun yang lalu
5.0
$100.00 USD
Project for Aditya Vikram S.
Excellent Work, Good freelancer
Python
Web Scraping
Natural Language
OpenCV
+1 lagi
P
Closed User
@patrickquentin1
•
3 tahun yang lalu
5.0
$15.00 USD
Project for Aditya Vikram S.
Aditya is excellent Machine Learning developer. Please hire Aditya if anyone have project. To complete my project he was best. Thank
Python
Web Scraping
Natural Language
OpenCV
+1 lagi
A
Closed User
@aleksandrruban
•
4 tahun yang lalu

Pengalaman

Data Scientist

XYZ
Ogo 2022 - Hari ini
-> Developed a Fall-Risk model Hybrid Model that accurately predicts the fall risk score for patients. It assists healthcare professionals in identifying patients who require extensive assistance to prevent falls. -> Contributed to the development of multiple microservices utilizing Kafka, Postgres, Snowflake, and FastAPI. Kafka is effectively employed to trigger events across various interconnected services.

Data Scientist (Freelancer)

Rüdiger Wöhrl GmbH
Okt 2021 - Feb 2024 (2 tahun, 4 bulan)
ANPR: Automatic Number Plate Detection (Python, YOLO-V4, CRAFT, CRNN) -> Detecting the license plate number of vehicles of all European countries. Deployed the model on RPi4 and ODroid board. The accuracy of ANPR (deployed on RPi4) is 94% of accuracy. Container Number Detection (Python, YOLO-V4, CRAFT, Classifier) -> Detecting the container number of shipping containers and deployed the model on RPi4 and ODroid board. The overall accuracy of this model is 95%.

Data Analyst

ByteLearn
Nov 2021 - Sep 2022 (10 bulan, 1 hari)
Solver Building (Python, Low-Code Platform) Lead a team of 10 people to build the solvers for circles and percent-based problems using Low-Code platform and Python. Object Detection (Python, YOLO-V5, OpenCV, AWS) Build an object detection model for detecting the position of problem figure & text region and deployed the model on AWS.

Pendidikan

B.Tech

B. M. S. College of Engineering, India 2015 - 2019
(4 tahun)

Kelayakan

Deep Learning by Andrew Ng

Coursera
2020
In this certificate, I have through five interconnected courses and developed a profound knowledge of the hottest AI algorithms, mastering deep learning from its foundations (neural networks) to its industry applications (Computer Vision, Natural Language Processing, Speech Recognition, etc.).

IBM Data Science

Coursera
2019
In this Professional Certificate, I have developed and honed hands-on skills in Data Science and Machine Learning. I have started with an orientation of Data Science and its Methodology, became familiar and used a variety of data science tools, learned Python and SQL, performed Data Visualization and Analysis, and created Machine Learning models.

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Kemahiran Teratas

Python 9
Web Scraping 7
Natural Language 7
OpenCV 6
Tensorflow 6

Layari Pekerja Bebas Serupa

Python Developers in India
Python Developers
Web Scraping Specialists
Linguists

Layari Pameran Serupa

Python
Web Scraping
Natural Language
OpenCV
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