Introducing docTR: Optical Character Recognition Made Easy
Introduction
docTR is an innovative project that leverages the power of TensorFlow 2 and PyTorch to provide seamless and accessible Optical Character Recognition (OCR) for everyone. This project enables efficient parsing of textual information from documents, and offers guidance for integrating OCR into existing systems.
Key Features
docTR offers a two-stage approach to OCR:
- Text Detection: Identifying and localizing words in a document.
- Text Recognition: Recognizing the characters within the localized words.
With docTR, users have the flexibility to choose from a variety of models for these tasks, ensuring optimal performance tailored to specific needs.
Getting Started
Pretrained Models
Users can easily get started with docTR by loading pretrained models. This can be done with a simple Python script that initializes the OCR model to both detect and recognize text.
Document Reading
docTR supports reading documents from multiple sources, including PDFs, images, and web pages. This versatility enables it to handle a wide range of document types efficiently.
Handling Rotated Documents
For documents with rotated pages, docTR offers options to accommodate various orientations. Users can configure the model to process pages with rotated text or opt for faster processing by assuming all text is horizontally aligned.
Advanced Features
KIE Predictor
The Key Information Extraction (KIE) predictor extends the capabilities of OCR by allowing the detection of multiple classes within a document, such as dates or addresses, alongside text recognition.
Visualization and Synthesis
docTR provides tools for visualizing OCR results, allowing users to interactively explore the recognized text. It also enables the reconstruction of original documents from predictions, offering a comprehensive view of the OCR process.
Installation
docTR requires Python 3.9 or higher. It can be installed using pip, with optional dependencies for TensorFlow or PyTorch, depending on the user's preference and system capabilities. For those with specific requirements, such as MacBooks with M1 chips, additional steps might be necessary.
Use Cases
- Demo App: Users can explore docTR's capabilities with a minimal demo app.
- Live Demo: A fully-deployed version of docTR is available on Hugging Face Spaces for an online demonstration.
- TensorFlow.js Demo: Users can run OCR directly in their web browsers with a specialized demo using TensorFlow.js.
Docker and Examples
docTR provides Docker support for easy testing and deployment, and offers example scripts and notebooks to illustrate its comprehensive features. For integration into APIs, a FastAPI framework template is available.
Contribution and Licensing
Mindee encourages contributions to the docTR project. Detailed guides are available to assist users in extending or improving the project. docTR is distributed under the Apache 2.0 License, ensuring it remains free and open for the community.
Conclusion
docTR simplifies the integration and deployment of Optical Character Recognition, making advanced text detection and recognition accessible to developers across various domains. With its robust features and supportive community, docTR stands as a powerful tool in the world of document processing.