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Documents-Parsing-Lab

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

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# 📝 Documents Parsing Lab
![The Parsing Lab: OCR & Document Intelligence](https://raw.githubusercontent.com/AdemBoukhris457/Documents-Parsing-Lab/main/assets/banner.png)

> 🌐 **Language / Idioma:** **English** · [Español](README.es-ES.md)

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.

---

## 🚀 Doctra Quick Start

This section provides a quick start guide for getting started with **Doctra**, a powerful tool for structured document parsing without Vision Language Models (VLM). Learn how to parse PDF documents, extract structured content (text, tables, charts, figures), and generate multiple output formats.

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## 📚 Notebooks Overview

| Notebook                                                                                                                                                                            | Description                                                         |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
| [bytedance-dolphin-image-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/bytedance-dolphin-image-parsing.ipynb)                                 | Document page parsing with **Dolphin** by ByteDance                 |
| [Llama-3.1-Nemotron-Nano-VL-8B-V1_parsing_documents.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/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](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/docling-documents-parsing-and-tables-extraction.ipynb) | Parsing and table extraction with **Docling**                       |
| [typhoon-ocr-7b-docs-pages-parser.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/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](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/florence-2-large-ocr-documents-pages.ipynb)                       | OCR of document pages using **Florence 2 Large**                    |
| [florence-2-large-ocr-images-real-life-scenarios.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/florence-2-large-ocr-images-real-life-scenarios.ipynb) | Real-life scenario OCR with **Florence 2 Large**                    |
| [got-ocr2-0-docs-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/got-ocr2-0-docs-parsing.ipynb)                                                 | Document pages parsing with **GOT-OCR2.0** and **Gemini 2.5 Flash** |
| [marker-docs-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/marker-docs-parsing.ipynb)                                                         | Marker-based document parsing experiments                           |
| [mistralocr-docs-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/mistralocr-docs-parsing.ipynb)                                                 | Document parsing using **MistralOCR**                               |
| [monkeyocr-docs-pages-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/monkeyocr-docs-pages-parsing.ipynb)                                       | Document parsing with **MonkeyOCR**                                 |
| [nanonets-OCR-s\_docs\_parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/Nanonets-OCR-s_docs_parsing.ipynb)                                       | Advanced document parsing using **Nanonets-OCR-s**                  |
| [ollama-llama3-2-vision-usage.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/ollama-llama3-2-vision-usage.ipynb)                                       | Using **Llama3-2 Vision** for document parsing                      |
| [paddleocr-3-0-docs-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/paddleocr-3-0-docs-parsing.ipynb)                                           | Parsing with **PaddleOCR 3.0** PP-StructureV3                       |
| [pix2text-docs-pages-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/pix2text-docs-pages-parsing.ipynb)                                         | Document parsing using **Pix2Text**                                 |
| [smoldocling-documents-understanding.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/smoldocling-documents-understanding.ipynb)                         | Document understanding with **SmolDocling**                         |
| [zerox-pdf-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/zerox-pdf-parsing.ipynb)                                                             | PDF parsing experiments with **Zerox**                              |
| [qwen2-vl-2b-docs-parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/qwen2-vl-2b-docs-parsing.ipynb)                                                             | Documents pages parsing with **Qwen2-VL-2B**                              |
| [OCRFlux_3B_Docs_Parsing.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/OCRFlux_3B_Docs_Parsing.ipynb)                                                             | Document parsing with **OCRFlux-3B** on Lightning AI                              |
| [granite-docling-258m-document-parsing-review.ipynb](https://github.com/AdemBoukhris457/Documents-Parsing-Lab/blob/main/granite-docling-258m-document-parsing-review.ipynb)                                                             | Evaluating **IBM Granite DocLing 258M** for document parsing and layout understanding                              |

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## 📑📊 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.

---

## 📑🔍 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.

---

## 📖 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:**

```bash
   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](https://www.kaggle.com/ademboukhris/code)

---

## Star History

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Documents-Parsing-Lab

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