Try to help in translation from English to Arabic or vice versa -- 2

@lolo2222

Hello sir, Please be assured I am the best suited to do this task. I have a very good experience in translation. Also, I am a native Arabic speaker & I have a very good command of the English language, both written & verbal. I am an experienced translator with strong skills in translating documents, articles, business communication, and multimedia content between Arabic and English. My work focuses on delivering accurate, culturally appropriate, and natural translations while maintaining the original meaning and tone. I have experience handling administrative documents, educational materials, marketing content, and general translation projects with attention to detail and confidentiality. In addition to translation, I am skilled in proofreading, editing, transcription, and localization to ensure high-quality final content. I am proficient in meeting deadlines, managing multiple projects, and communicating professionally with clients and teams. My goal is always to provide clear, polished, and reliable translations that support effective communication across languages. Please message me immediately to discuss further details and start right away. Thanks …

@mayankinnovative

Dear Hiring Manager, As per my understanding: You need an Arabic PDF chapter converted into structured, searchable data with OCR cleanup, layout analysis, image/table extraction, and JSON schema for semantic search + RAG systems. Implementation approach: I'll leverage Python with tools like pdfplumber, Tesseract-OCR (Arabic), and custom NER for concept extraction. Data will be chunked, vectorized, and output as JSON with page references. Key highlights: → Arabic OCR cleanup and mixed text handling → PDF layout analysis, image/table extraction → JSON schema with concepts, questions, page refs → Data prepared for semantic search, vector search, RAG → Quality report, docs, and sample queries/demo Required tools: pdfplumber, Tesseract-OCR (ara), spaCy, custom JSON schema Sample JSON schema and approach details available on request. A few quick questions: 1. Chapter length (pages) and typical layout (text-heavy/images/tables)? 2. Preferred JSON structure for concepts, chunks, and metadata? 3. Any specific RAG or search engine to integrate with? Best Regards, Mayank Saluja

@AlexVydrin

Hi, this is a great fit for the kind of structured‑data and RAG‑ready pipelines I build. I’ve worked with Arabic PDFs before, including OCR cleanup, mixed‑language extraction, table parsing and turning messy chapters into clean JSON that’s ready for embeddings and semantic search. My approach is to run high‑accuracy Arabic OCR, normalize the text, extract images and tables, then chunk the content with page‑level references and a schema that supports both search and downstream QA. You’d get structured JSON, extracted assets, search‑ready chunks, a small demo of how the data performs, plus clear documentation of tools, methodology and quality checks. If you want, I can share sample schemas and past Arabic OCR work so you can see the level of structure I deliver.

@omkumarsingh1

I've done this exact type of work before — Arabic PDF extraction with mixed Arabic/English content, OCR correction, and JSON structuring for downstream search systems. My stack for this project: pdfplumber + Camelot for layout and table extraction, EasyOCR / Tesseract with Arabic language model for OCR, custom Python scripts for chunk splitting and schema generation, and FAISS or ChromaDB for search-ready embedding prep. For the JSON schema, each chunk gets: page reference, section title, content type (text/table/image), raw text, cleaned text, and metadata fields for RAG ingestion. Images get extracted separately with page-level naming. I'll deliver: structured JSON files, extracted images and tables in organized folders, search-ready chunks with overlap, a sample schema, and a short quality report flagging any OCR confidence issues. Timeline for one chapter: 2–3 days. I work carefully on Arabic content because RTL layout and mixed-script tables break most off-the-shelf tools. I handle that manually where needed. Happy to share a sample schema now if it helps you evaluate.

@chaoc19

Hi there, I've built solutions for data modeling before — this is right in my wheelhouse. Here's how I'll approach it: 1) Review full source material for context 2) Translate with natural tone and accurate terminology 3) Proofread and deliver Timeline: 2 day(s) | Bid: $5 Full payment only when you're satisfied. If the work doesn't meet your standards, you don't pay. Ready to start now. Message me and let's get going.

@ambreenr1

Hey I hope you are doing great. My profile might look new but I have done these works earlier. I have good research and AI usage techs with me. Also I understand Arabic well. If you give me a text here we can discuss and I can do your project in best manner and in lowest budget, Thanks.

@samiullam1

Hello, I have experience with Arabic PDF extraction, OCR cleanup, Python processing, and AI-ready data structuring. I understand the challenges of mixed Arabic/English text, layout analysis, table extraction, and preparing clean data for semantic search and RAG systems. For this proof-of-concept chapter, I will provide: * Structured JSON output * OCR-cleaned Arabic text * Extracted images and tables * Search-ready chunks with page references * Concept/question extraction if available * Documentation and quality report Tools: Python, PyMuPDF, pdfplumber, PaddleOCR/Tesseract, LangChain, JSON schema tools. Sample JSON: { "page": 12, "title": "", "content": "", "concepts": [], "questions": [] } Estimated timeline: 2–3 days. I focus on accuracy, clean structure, and making the final output truly usable for AI search and vector databases. Best regards, Sami

@ELGAMLDev

Hi I understand the main challenge here is not just extracting Arabic text from the PDF but keeping the structure clean and usable for semantic search and RAG later especially with mixed Arabic and English content scanned pages tables and images. My approach would be to first analyze the PDF layout then process the OCR carefully to preserve the correct reading order and clean Arabic text properly. After that I would structure the content into searchable chunks with page references metadata extracted tables and images and prepare everything in JSON format ready for embeddings and vector search systems. I usually work with Python tools like PyMuPDF pdfplumber PaddleOCR OpenCV and Pandas depending on the document quality and structure. The final delivery would include structured JSON files extracted images and tables search ready chunks methodology documentation a quality report and a simple semantic search demo or sample queries. For one chapter the estimated timeline would usually be around 1 to 3 days depending on the scan quality and layout complexity.