A powerful OCR (Optical Character Recognition) package that uses state-of-the-art vision language models through Ollama to extract text from images and PDF. Available both as a Python package and a Streamlit web application.
## 🌟 Features
### Supports PDF and Images (New! 🆕)
- **Multiple Vision Models Support**
- [LLaVA](https://ollama.com/library/llava): Efficient vision-language model for real-time processing (LLaVa model can generate wrong output sometimes)
- Llama 3.2 Vision: Advanced model with high accuracy for complex documents
- [Granite3.2-vision](https://ollama.com/library/granite3.2-vision): A compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more.
- [Moondream](https://ollama.com/library/moondream): Small vision language model designed to run efficiently on edge devices.
- [Minicpm-v](https://ollama.com/library/minicpm-v): MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344).
- **Multiple Output Formats**
- Markdown: Preserves text formatting with headers and lists
- Plain Text: Clean, simple text extraction
- JSON: Structured data format
- Structured: Tables and organized data
- Key-Value Pairs: Extracts labeled information
- Table: Extract all tabular data.
- **Batch Processing**
- Process multiple images in parallel
- Progress tracking for each image
- Image preprocessing (resize, normalize, etc.)
- **Custom Prompts**
- Override default prompts with custom instructions for text extraction.
## 📦 Package Installation
```bash
pip install ollama-ocr
```
## 🚀 Quick Start
### Prerequisites
1. Install Ollama
2. Pull the required model:
```bash
ollama pull llama3.2-vision:11b
ollama pull granite3.2-vision
ollama pull moondream
ollama pull minicpm-v
```
## Using the Package
### Single File Processing
```python
from ollama_ocr import OCRProcessor
# Initialize OCR processor
ocr = OCRProcessor(model_name='llama3.2-vision:11b', base_url="http://host.docker.internal:11434/api/generate") # You can use any vision model available on Ollama
# you can pass your custom ollama api
# Process an image
result = ocr.process_image(
image_path="path/to/your/image.png", # path to your pdf files "path/to/your/file.pdf"
format_type="markdown", # Options: markdown, text, json, structured, key_value
custom_prompt="Extract all text, focusing on dates and names.", # Optional custom prompt
language="English" # Specify the language of the text (New! 🆕)
)
print(result)
```
### Batch File
```python
from ollama_ocr import OCRProcessor
# Initialize OCR processor
ocr = OCRProcessor(model_name='llama3.2-vision:11b', max_workers=4) # max workers for parallel processing
# Process multiple images
# Process multiple images with progress tracking
batch_results = ocr.process_batch(
input_path="path/to/images/folder", # Directory or list of image paths
format_type="markdown",
recursive=True, # Search subdirectories
preprocess=True, # Enable image preprocessing
custom_prompt="Extract all text, focusing on dates and names.", # Optional custom prompt
language="English" # Specify the language of the text (New! 🆕)
)
# Access results
for file_path, text in batch_results['results'].items():
print(f"\nFile: {file_path}")
print(f"Extracted Text: {text}")
# View statistics
print("\nProcessing Statistics:")
print(f"Total images: {batch_results['statistics']['total']}")
print(f"Successfully processed: {batch_results['statistics']['successful']}")
print(f"Failed: {batch_results['statistics']['failed']}")
```
## 📋 Output Format Details
1. **Markdown Format**: The output is a markdown string containing the extracted text from the image.
2. **Text Format**: The output is a plain text string containing the extracted text from the image.
3. **JSON Format**: The output is a JSON object containing the extracted text from the image.
4. **Structured Format**: The output is a structured object containing the extracted text from the image.
5. **Key-Value Format**: The output is a dictionary containing the extracted text from the image.
6. **Table Format**: Extract all tabular data.
-----
## 🌐 Streamlit Web Application(supports batch processing)
- **User-Friendly Interface**
- Drag-and-drop file upload
- Real-time processing
- Download extracted text
- Image preview with details
- Responsive design
- Language Selection: Specify the language for better OCR accuracy. (New! 🆕)
1. Clone the repository:
```bash
git clone https://github.com/imanoop7/Ollama-OCR.git
cd Ollama-OCR
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Go to the directory where app.py is located:
```bash
cd src/ollama_ocr
```
3. Run the Streamlit app:
```bash
streamlit run app.py
```
## 📒 Example Notebooks
- [Ollama OCR on Colab](example_notebooks\ollama_ocr_on_colab.ipynb): How to use Ollama-OCR on Google Colab.
- [Example Notebook](example_notebooks\example.ipynb): Example usage of Ollama OCR.
- [Ollama OCR with Autogen](example_notebooks\ollama-ocr-with-autogen.ipynb): Use Ollama-OCR with autogen.
- [Ollama OCR with LangGraph](example_notebooks\ollama-ocr-with-langgraph.ipynb): Use Ollama-OCR with LangGraph.
## Examples Output
### Input Image
### Sample Output
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🙏 Acknowledgments
Built with Ollama
Powered by Vision Models
## Star History
