🤖 OCR Language Classifier — Telegram Bot

A modular, Dockerized Telegram bot for OCR-based document text extraction and language identification.
Built with MMOCR (TextSnake), Tesseract, and FastText, this system leverages both computer vision and natural language processing.

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✨ Features

✅ Extracts text from user-submitted images
✅ Performs language detection using FastText (176+ languages)
✅ Uses MMOCR for text detection and Tesseract for OCR
✅ Fully Dockerized with Miniconda
✅ Async Telegram bot built with Aiogram
✅ Easily extendable and open-source

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🗂 Project Structure

ocr-documents-system/
├── bot/
│   ├── handler/
│   │   ├── commands.py       # /start, /help, /list handlers
│   │   └── image.py          # Image recognition handler
│   ├── config.py             # Bot configuration (Telegram token)
│   ├── logger.py             # Logging setup
│   └── main.py               # Bot startup entry point
│
├── ocr_engine/
│   ├── classifier.py         # FastText-based language classifier
│   ├── config.py             # OCRConfig dataclass for paths
│   ├── engine.py             # Main OCREngine pipeline (TextSnake + Tesseract)
│   ├── lang_map.py           # Mapping language code → name (Russian)
│   └── utils.py              # Helper functions & language list
│
├── data/
│   ├── models/               # FastText `.bin` model
│   └── tessdata/             # Tesseract language files
│
├── .env                      # Environment variables
├── Dockerfile                # Conda-based Docker build
├── .dockerignore             # Ignoring unnecessary local files
├── .gitignore                # Standard exclusions + /data
└── README.md                 # You're reading it

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🖼️ Demo Screenshots

💬 Command Interface

Interact using:

• /start
• /help
• /list

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🌍 Language Detection Examples

🇫🇮 Finnish Document

🇷🇺 Russian Document

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⚙️ Environment Variables

Create a .env file in the root directory with the following values:

BOT_TOKEN=your_telegram_bot_token
FASTTEXT_PATH=/ocr-documents-system/data/models/lid.176.bin
TESSDATA_PATH=/ocr-documents-system/data/tessdata

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🐳 Running with Docker

docker build -t ocr-bot .
docker run --env-file .env ocr-bot

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📦 Technologies

• MMOCR (TextSnake)
• Tesseract OCR (tesserocr wrapper)
• FastText for language prediction
• Aiogram Telegram bot framework
• Docker + Miniconda for reproducible environments

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🧪 Example Output

Input: Scanned image of a document
Output:

• Extracted text
• Predicted language
• Inline message in Telegram

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📜 License

This project is licensed under the MIT License.

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✉ Contact

🐙 GitHub: AlekseyScorpi
📬 For questions or collaborations — feel free to reach out via GitHub Issues or Pull Requests.