OpenMLkit OCR

A lightweight, offline Python OCR (Optical Character Recognition) library utilizing highly optimized, mobile-ready Google ML Kit TFLite models. It performs text detection using a Region Proposal Network (RPN) and line recognition using a CRNN-CTC architecture.

Features

• Fully Offline: Runs entirely local, no API keys or internet connection required after downloading models.
• Multilingual Support: Supports 15+ languages and scripts (Cyrillic/Russian, Latin/English, Chinese, Japanese, Korean, Arabic, Hebrew, and various Indian scripts).
• Auto-downloading: Automatically downloads and caches required models from Hugging Face if they are not present locally.
• High Quality Stitching: Handles wide text lines without squishing by dividing them into overlapping chunks and merging them using fuzzy suffix-prefix alignment.

Installation

Install the package directly using pip:

pip install openmlkitOCR

Or install from source:

git clone https://github.com/0cve0/OpenMLkitOCR.git
cd OpenMLkitOCR
pip install -e .

Quick Start

import os
import cv2
from openmlkit import OpenMLKitOCR

# Configure Hugging Face model source (or use defaults)
os.environ["OPENMLKIT_MODEL_REPO"] = "0cve0/OpenMLKitOCR"

# Initialize OCR pipeline for Cyrillic (Russian) text
ocr = OpenMLKitOCR(lang='ru')

# Load image
img = cv2.imread("scratch/russian_test.png")

# Run OCR (detect and recognize text)
results = ocr.run(img, score_threshold=0.35)

# Output results
for r in results:
    print(f"Box: {r['box']} -> Text: {r['text']}")

Project Structure

• openmlkit/ - Core Python package directory.
  • detector.py - RPN text detection logic.
  • recognizer.py - CRNN text recognition logic.
  • labelmap.py - Parse binary protobuf label maps.
  • pipeline.py - OCR pipeline joining detection, tiling, and recognition.

License

This project is licensed under the Apache 2.0 License. The model weights are subject to Google's terms and licenses for ML Kit.