Documents-Parsing-Lab

Documents-Parsing-Lab

Jupyter notebooks testing different OCR models for document parsing (Dolphin, MonkeyOCR, Marker, Nanonets, ...)

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A curated collection of Jupyter notebooks for experimenting with state-of-the-art OCR, document parsing, table extraction, and chart understanding techniques. This repository enables easy benchmarking and practical usage of the latest open-source and cloud-based solutions for document image processing.

README:

📝 Documents Parsing Lab

The Parsing Lab: OCR & Document Intelligence

A curated collection of Jupyter notebooks for experimenting with state-of-the-art OCR, document parsing, table extraction, and chart understanding techniques. This repository enables easy benchmarking and practical usage of the latest open-source and cloud-based solutions for document image processing.


🚀 Notebooks Overview

Notebook Description
bytedance-dolphin-image-parsing.ipynb Document page parsing with Dolphin by ByteDance
Llama-3.1-Nemotron-Nano-VL-8B-V1_parsing_documents.ipynb Testing the performance of document parsing with Llama-3.1-Nemotron-Nano-VL-8B-V1
docling-documents-parsing-and-tables-extraction.ipynb Parsing and table extraction with Docling
typhoon-ocr-7b-docs-pages-parser.ipynb Evaluating Typhoon_ocr_7b Document Parsing Capabilities Across Various Use Cases
florence-2-large-ocr-documents-pages.ipynb OCR of document pages using Florence 2 Large
florence-2-large-ocr-images-real-life-scenarios.ipynb Real-life scenario OCR with Florence 2 Large
got-ocr2-0-docs-parsing.ipynb Document pages parsing with GOT-OCR2.0 and Gemini 2.5 Flash
marker-docs-parsing.ipynb Marker-based document parsing experiments
mistralocr-docs-parsing.ipynb Document parsing using MistralOCR
monkeyocr-docs-pages-parsing.ipynb Document parsing with MonkeyOCR
nanonets-OCR-s_docs_parsing.ipynb Advanced document parsing using Nanonets-OCR-s
ollama-llama3-2-vision-usage.ipynb Using Llama3-2 Vision for document parsing
paddleocr-3-0-docs-parsing.ipynb Parsing with PaddleOCR 3.0 PP-StructureV3
pix2text-docs-pages-parsing.ipynb Document parsing using Pix2Text
smoldocling-documents-understanding.ipynb Document understanding with SmolDocling
zerox-pdf-parsing.ipynb PDF parsing experiments with Zerox
qwen2-vl-2b-docs-parsing.ipynb Documents pages parsing with Qwen2-VL-2B
OCRFlux_3B_Docs_Parsing.ipynb Document parsing with OCRFlux-3B on Lightning AI
granite-docling-258m-document-parsing-review.ipynb Evaluating IBM Granite DocLing 258M for document parsing and layout understanding

📑📊 Tables and Charts Recognition

This section includes notebooks focused on table and chart detection, structure recognition, and extraction from documents. It covers various open-source approaches and benchmarks for understanding table and chart layouts and content.

Notebook Description
unitable-testing-for-table-structure-recognition.ipynb Testing table detection and structure recognition with UniTable
deepdoctection-tables-recognition.ipynb Evaluating Deepdoctection for table extraction across varied structures
gemini-2-5-pro-on-chart-and-table-extraction.ipynb Chart/table extraction using Gemini 2.5 Pro
deplot-plots-to-tables-converter.ipynb Converting Charts into Tables with DePlot
cohere-command-a-vision-charts-understanding.ipynb Cohere Command A Vision for Charts Understanding
cohere-command-a-vision-tables-recognition.ipynb Cohere Command A Vision for Tables Recognition
moondream2-charts-tables-interpretation.ipynb Moondream2 for Charts and Tables understanding

📑🔍 Structured Data Extraction

This section covers the structured data extraction phase, detailing methods to extract specific data from documents or images. It includes steps like OCR preprocessing, table extraction, named entity recognition (NER), and conversion to structured formats.

Notebook Description
NuExtract-2-8b-structured-data-extraction NuExtract-2.0-8B for Structured Data Extraction

📖 Project Goals

  • Benchmark different OCR/document parsing models on real documents.
  • Demonstrate table, chart, and text extraction workflows.
  • Compare open-source and commercial solutions.
  • Provide ready-to-use code snippets for rapid prototyping.

🛠️ Usage

  1. Clone the repository:

    git clone https://github.com/AdemBoukhris457/Docs_Parsing_Techniques.git
  2. Install dependencies as needed for each notebook (see the first cells of each .ipynb for requirements).

  3. Launch Jupyter Notebook or JupyterLab and open any notebook of interest.

  4. Run the cells and adapt the code for your documents.


📌 Notes

  • Some notebooks require model weights or API keys, check comments in each notebook for details.
  • Results, insights, and sample outputs are provided inline.

🔗 Related Resources

📂 You can find more notebooks, experiments, and datasets related to document parsing and OCR on my Kaggle profile: 👉 https://www.kaggle.com/ademboukhris/code


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