Core-OCR

Core-OCR is an advanced, experimental Optical Character Recognition (OCR) and document analysis suite designed for highly accurate text extraction, table reconstruction, and complex visual reasoning. Built on the robust Qwen2.5-VL and Qwen2-VL multimodal architectures, this application provides a modern, interactive web interface capable of processing a wide array of visual inputs, from standard documents and multilingual texts to structured tables and complex scene images. By integrating specialized state-of-the-art vision-language models—such as Camel-Doc-OCR, docscopeOCR, MonkeyOCR, and coreOCR—the tool empowers users to precisely reconstruct document formatting, perform fill-in-the-blank number extraction, and generate detailed scene explanations. The suite is fully GPU-accelerated and offers granular control over text generation parameters, creating an optimal environment for deploying and testing robust document intelligence workflows.

Key Features

• Multi-Model Architecture: Seamlessly switch between specialized vision-language models directly from the interface. Supported models include Camel-Doc-OCR-080125(v2), docscopeOCR-7B-050425-exp, MonkeyOCR-Recognition, and coreOCR-7B-050325-preview.
• Custom User Interface: Features a bespoke, responsive Gradio frontend built with custom HTML, CSS, and JavaScript. It includes a drag-and-drop media zone, real-time output streaming, and an integrated advanced settings panel.
• Granular Inference Controls: Fine-tune the AI's output by adjusting text generation parameters such as Maximum New Tokens, Temperature, Top-p, Top-k, and Repetition Penalty.
• Output Management: Built-in actions allow users to instantly copy the raw output text to their clipboard or save the generated response directly as a .txt file.
• Flash Attention 2 Integration: Utilizes kernels-community/flash-attn2 for optimized, memory-efficient inference on compatible GPUs.

Repository Structure

├── images/
│   ├── 0.png
│   ├── doc.jpg
│   ├── image1.png
│   ├── image2.jpg
│   ├── image3.png
│   └── zh.png
├── app.py
├── LICENSE
├── pre-requirements.txt
├── README.md
└── requirements.txt

Installation and Requirements

To run Core-OCR locally, you need to configure a Python environment with the following dependencies. Ensure you have a compatible CUDA-enabled GPU for optimal performance.

1. Install Pre-requirements Run the following command to update pip to the required version:

pip install pip>=23.0.0

2. Install Core Requirements Install the necessary machine learning and UI libraries. You can place these in a requirements.txt file and run pip install -r requirements.txt.

git+https://github.com/huggingface/transformers.git@v4.57.6
git+https://github.com/huggingface/accelerate.git
git+https://github.com/huggingface/peft.git
transformers-stream-generator
huggingface_hub
qwen-vl-utils
sentencepiece
opencv-python
torch==2.8.0
torchvision
matplotlib
requests
kernels
hf_xet
spaces
pillow
gradio
av

Usage

Once your environment is set up and the dependencies are installed, you can launch the application by running the main Python script:

python app.py

After the script initializes the interface, it will provide a local web address (usually http://127.0.0.1:7860/) which you can open in your browser to interact with the models. Note that the selected models will be downloaded and loaded into VRAM upon their first invocation.

License and Source

• License: Apache License - Version 2.0
• GitHub Repository: https://github.com/PRITHIVSAKTHIUR/Core-OCR