thulium-htr 1.2.1

Thulium - State-of-the-Art Multilingual Handwriting Text Recognition for Python

Thulium

State-of-the-art multilingual handwriting text recognition.

Thulium is a production-ready Python library for offline handwritten text recognition (HTR) supporting 52+ languages across Latin, Cyrillic, Greek, Arabic, Hebrew, Devanagari, Chinese, Japanese, Korean, and Georgian scripts.

Features

• 52+ Languages — Comprehensive multilingual support with script-aware processing
• Production Ready — Optimized inference with ONNX export and mixed precision
• State-of-the-Art — CNN/ViT backbones with Transformer/LSTM sequence heads
• Explainable AI — Attention visualization, saliency maps, and confidence analysis
• Flexible Decoding — CTC beam search with n-gram and neural language models

Installation

pip install thulium

For GPU acceleration:

pip install thulium[gpu]

Quick Start

from thulium import recognize_image

# Single image recognition
result = recognize_image("document.png", language="en")
print(result.text)

# Batch recognition with confidence scores
from thulium import HTRPipeline

pipeline = HTRPipeline.from_pretrained("thulium-base-multilingual")
results = pipeline.recognize_batch(images, languages=["en", "de", "fr"])

for r in results:
    print(f"{r.text} (confidence: {r.confidence:.2%})")

Supported Languages

52+ languages across 10 scripts (click to expand)

Region	Languages
Western Europe	English, German, French, Spanish, Italian, Portuguese, Dutch
Scandinavia	Swedish, Norwegian, Danish, Finnish, Icelandic
Eastern Europe	Polish, Czech, Hungarian, Romanian, Bulgarian, Ukrainian, Russian
Baltic	Lithuanian, Latvian, Estonian
Caucasus	Georgian, Armenian, Azerbaijani
Middle East	Arabic, Hebrew, Persian, Turkish
South Asia	Hindi, Bengali, Tamil, Telugu, Urdu
East Asia	Chinese, Japanese, Korean

Documentation

Guide	Description
Getting Started	Installation and first steps
API Reference	Complete API documentation
Model Zoo	Pretrained model catalog
Training Guide	Train custom models
Architecture	System design overview

Performance

Benchmarks on IAM Handwriting Database:

Model	CER	WER	Latency
thulium-tiny	5.2%	14.1%	12ms
thulium-base	3.8%	10.2%	28ms
thulium-large	2.9%	7.8%	65ms

Measured on NVIDIA A100, batch size 1, PyTorch 2.0+

Citation

@software{thulium2025,
  title={Thulium: Multilingual Handwriting Recognition},
  author={Thulium Authors},
  year={2025},
  url={https://github.com/thulium-dev/thulium}
}

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

Apache 2.0 — see LICENSE for details.