thulium-htr 1.0.2

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

Thulium HTR

State-of-the-Art Multilingual Handwriting Text Recognition

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Thulium is a production-grade, research-oriented Python framework for offline handwritten text recognition (HTR). The library implements state-of-the-art deep learning architectures and provides comprehensive support for 56 languages across 12 distinct writing systems.

Version 1.0.2: Production-ready release with complete documentation and community standards.

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Table of Contents

• Overview
• Installation
• Quickstart
• Architecture
• Supported Languages
• Evaluation Metrics
• Benchmarks
• API Reference
• Contributing
• License

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Overview

Thulium addresses the fundamental challenges of multilingual handwriting recognition through a modular, configurable architecture that supports both research experimentation and production deployment.

Core Capabilities

Capability	Description
Multilingual Recognition	56 languages across Latin, Cyrillic, Arabic, Devanagari, Georgian, Armenian, CJK, and other scripts
SoTA Architectures	CNN-RNN-CTC, Vision Transformer (ViT), Conformer, and attention-based seq2seq models
Language Model Integration	N-gram and neural language models for enhanced decoding accuracy
Production-Ready	Optimized inference, batch processing, and comprehensive error handling
Research-Oriented	Modular components, configurable pipelines, and reproducible experiments

Design Principles

1. Language Parity: Every supported language receives equal treatment in terms of model coverage, configuration, and documentation.
2. Modularity: Components (backbones, sequence heads, decoders, language models) are interchangeable and configurable.
3. Reproducibility: All experiments are fully specified through YAML configurations with fixed random seeds.
4. Extensibility: New languages, models, and evaluation metrics can be added with minimal code changes.

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Installation

From PyPI

pip install thulium-htr

From Source

git clone https://github.com/olaflaitinen/Thulium.git
cd Thulium
pip install -e .[dev]

Requirements

Requirement	Version
Python	3.10+
PyTorch	2.0+
CUDA (optional)	11.8+

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Quickstart

Python API

from thulium.api import recognize_image

# Recognize handwritten text
result = recognize_image(
    path="document.jpg",
    language="en",
    device="auto"
)

print(result.full_text)

Command-Line Interface

# Basic recognition
thulium recognize document.jpg --language en --output result.json

# Batch processing
thulium recognize input_dir/ --language de --output-dir results/

# Run benchmarks
thulium benchmark run config/eval/iam_en.yaml

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Architecture

Thulium implements a modular pipeline architecture where each component can be independently configured and replaced.

System Architecture

graph TB
    subgraph Input Layer
        A[Document Image]
        B[PDF Document]
    end
    
    subgraph Preprocessing
        C[Normalization]
        D[Binarization]
        E[Deskewing]
    end
    
    subgraph Segmentation
        F[Layout Analysis]
        G[Line Detection]
        H[Word Segmentation]
    end
    
    subgraph Recognition
        I[CNN/ViT Backbone]
        J[Sequence Head]
        K[Decoder]
    end
    
    subgraph Post-processing
        L[Language Model]
        M[Spell Correction]
        N[Output Formatting]
    end
    
    A --> C
    B --> C
    C --> D --> E
    E --> F --> G --> H
    H --> I --> J --> K
    K --> L --> M --> N

Model Architecture

graph LR
    subgraph Backbone
        A1[ResNet-34]
        A2[ViT-Base]
        A3[Hybrid CNN-ViT]
    end
    
    subgraph Sequence Head
        B1[BiLSTM]
        B2[Transformer]
        B3[Conformer]
    end
    
    subgraph Decoder
        C1[CTC Greedy]
        C2[CTC Beam Search]
        C3[Attention Seq2Seq]
    end
    
    A1 --> B1 --> C1
    A2 --> B2 --> C2
    A3 --> B3 --> C3

Module Structure

Module	Purpose
thulium.api	High-level recognition API
thulium.models.backbones	Feature extraction (CNN, ViT)
thulium.models.sequence	Sequence modeling (LSTM, Transformer)
thulium.models.decoders	Output decoding (CTC, Attention)
thulium.models.language_models	Language model integration
thulium.pipeline	End-to-end processing pipelines
thulium.evaluation	Metrics and benchmarking
thulium.data	Data loading and language profiles

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Supported Languages

Thulium provides first-class support for 56 languages organized by regional groups.

Language Coverage by Script

Script	Languages	Direction
Latin	35+	LTR
Cyrillic	4	LTR
Arabic	3	RTL
Georgian	1	LTR
Armenian	1	LTR
Devanagari	2	LTR
CJK	3	LTR
Other Indic	4	LTR

Regional Groups

Scandinavian Languages (7)

Code	Language	Special Characters
nb	Norwegian Bokmal	ae, o-stroke, a-ring
nn	Norwegian Nynorsk	ae, o-stroke, a-ring
sv	Swedish	a-umlaut, o-umlaut, a-ring
da	Danish	ae, o-stroke, a-ring
is	Icelandic	eth, thorn, acute accents
fo	Faroese	eth, acute accents
fi	Finnish	a-umlaut, o-umlaut

Baltic Languages (3)

Code	Language	Special Characters
lt	Lithuanian	ogonek, caron, macron
lv	Latvian	macron, cedilla, caron
et	Estonian	a-umlaut, o-tilde, o-umlaut

Caucasus Region (4)

Code	Language	Script
az	Azerbaijani	Latin (extended)
tr	Turkish	Latin
ka	Georgian	Mkhedruli
hy	Armenian	Armenian

Western European (7)

Code	Language
en	English
de	German
fr	French
es	Spanish
pt	Portuguese
it	Italian
nl	Dutch

Eastern European (12)

Code	Language	Script
pl	Polish	Latin
cs	Czech	Latin
sk	Slovak	Latin
hu	Hungarian	Latin
ro	Romanian	Latin
hr	Croatian	Latin
sl	Slovenian	Latin
ru	Russian	Cyrillic
uk	Ukrainian	Cyrillic
bg	Bulgarian	Cyrillic
sr	Serbian	Cyrillic
el	Greek	Greek

Middle East (4)

Code	Language	Direction
ar	Arabic	RTL
fa	Persian	RTL
ur	Urdu	RTL
he	Hebrew	RTL

South Asia (9)

Code	Language	Script
hi	Hindi	Devanagari
mr	Marathi	Devanagari
bn	Bengali	Bengali
ta	Tamil	Tamil
te	Telugu	Telugu
gu	Gujarati	Gujarati
pa	Punjabi	Gurmukhi
kn	Kannada	Kannada
ml	Malayalam	Malayalam

East Asia (3)

Code	Language	Script
zh	Chinese	Han
ja	Japanese	Kana/Kanji
ko	Korean	Hangul

For complete language profile details, see Language Support Documentation.

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Evaluation Metrics

Thulium implements standard HTR evaluation metrics with mathematical rigor.

Character Error Rate (CER)

The Character Error Rate measures the edit distance at the character level:

CER = (S + D + I) / N

Where:

• S = Number of substitutions
• D = Number of deletions
• I = Number of insertions
• N = Total characters in reference

Word Error Rate (WER)

The Word Error Rate applies the same formula at the word level:

WER = (S_w + D_w + I_w) / N_w

Fairness Metrics

To ensure language parity, Thulium tracks cross-language performance variance:

Delta_CER = max(CER_l) - min(CER_l)
Sigma_CER = sqrt(sum((CER_l - mean_CER)^2) / L)

A lower Delta_CER indicates more balanced performance across languages.

Usage

from thulium.evaluation.metrics import cer, wer, cer_wer_batch

# Single pair
error_rate = cer("reference text", "recognized text")

# Batch evaluation
references = ["text one", "text two"]
hypotheses = ["text one", "text too"]
batch_cer, batch_wer = cer_wer_batch(references, hypotheses)

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Benchmarks

Per-Language Performance

Language	Script	CER (%)	WER (%)	Model
English	Latin	1.8	5.2	Latin Multilingual
German	Latin	2.1	6.0	Latin Multilingual
Norwegian	Latin	2.1	5.9	Latin Multilingual
Azerbaijani	Latin	2.2	6.2	Latin Multilingual
Russian	Cyrillic	2.5	6.8	Cyrillic Multilingual
Georgian	Georgian	3.5	8.2	Georgian Specialized
Arabic	Arabic	4.2	10.5	Arabic Multilingual
Chinese	Han	5.5	-	CJK Multilingual

For complete benchmark results, see Benchmark Documentation.

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API Reference

High-Level API

from thulium.api import recognize_image, recognize_batch

# Single image
result = recognize_image(path, language="en", device="auto")

# Batch processing
results = recognize_batch(paths, language="en", batch_size=16)

Pipeline API

from thulium.pipeline import HTRPipeline

pipeline = HTRPipeline.from_config("config/pipelines/htr_default.yaml")
result = pipeline.process(image, language="en")

Language Profiles

from thulium.data.language_profiles import (
    get_language_profile,
    list_supported_languages,
    get_languages_by_region,
)

# Get profile
profile = get_language_profile("az")
print(f"Alphabet size: {len(profile.alphabet)}")

# List by region
scandinavian = get_languages_by_region("Scandinavia")

For complete API documentation, see API Reference.

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Contributing

Contributions are welcome. Please refer to CONTRIBUTING.md for guidelines.

All contributors must adhere to the Code of Conduct.

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License

Apache License 2.0. See LICENSE for details.

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Citation

If you use Thulium in your research, please cite:

@software{thulium2024,
  title = {Thulium: State-of-the-Art Multilingual Handwriting Text Recognition},
  author = {Thulium Contributors},
  year = {2024},
  url = {https://github.com/olaflaitinen/Thulium}
}

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Thulium is named after element 69, symbolizing the specialized nature of multilingual handwriting intelligence.