ML Text Detection project, set up with collaborative engineering and product manager workflows in mind:

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ML Text Detection from Images & Documents

Overview

This project enables automatic detection and extraction of text from images, scanned documents, and PDFs using machine learning. It delivers core capabilities for document digitization, content search, and data extraction, supporting business workflows and automation.

Key Features

• Detects printed and handwritten text in images and scanned documents
• Supports multiple file formats: JPEG, PNG, PDF
• Modular ML pipeline: preprocessing, model inference, postprocessing
• REST API for real-time or batch text extraction
• Sample datasets and inference scripts included
• Extensible architecture for new models, datasets, and downstream tasks

Use Cases

• Document digitization for enterprises
• Searchable archives from scanned PDFs/images
• Automated data entry from invoices, forms, contracts
• Enhanced accessibility for images/documents

Project Structure

ml-text-detection/
├── docs/           # Product specs, roadmap, requirements, user stories
├── src/            # ML pipeline code and app API
├── tests/          # Automated tests
├── assets/         # Sample images, diagrams
├── .github/        # Issue & PR templates
├── requirements.txt, environment.yml
├── README.md, CONTRIBUTING.md, CODEOWNERS, CHANGELOG.md

Getting Started

1. Clone the Repository

   git clone https://github.com/your-org/ml-text-detection.git
   cd ml-text-detection

2. Install Dependencies

   pip install -r requirements.txt

   or

   conda env create -f environment.yml
   conda activate ml-text-detection

3. Run Sample Inference

   python src/inference/run_inference.py --image assets/images/sample.png

4. Explore API (Optional)

   python src/app/api.py
   # Visit http://localhost:5000/docs for OpenAPI interface

Documentation

• docs/roadmap.md: Project roadmap & milestones
• docs/requirements.md: Business/product requirements
• docs/spec/pm-business-context.md: PM context, user stories
• CHANGELOG.md: Release history

Collaboration

• Use GitHub Issues for bug reports, feature requests, PM spec reviews
• All contributions must follow CONTRIBUTING.md
• Ownership pages: See CODEOWNERS

Tech Stack

• ML Frameworks: TensorFlow, PyTorch, OpenCV, Tesseract
• Languages: Python
• API: FastAPI or Flask (extensible)
• Tools: Docker (optional), GitHub Actions for CI/CD

Contact & Team

• Maintainers: @Engineering, @ProductManager
• Slack: #ml-text-detection
• Please reach out with questions or feature suggestions!

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License

See LICENSE for details.
All sample images and documents are for illustrative purposes only.

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This README is intended to be a living document.
Please update it as new features, workflows, or collaborators are added!