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PellelNitram/
xournalpp-htr-simple-htr

Name: xournalpp-htr-simple-htr
Availability: InStock

onnx

PellelNitram/xournalpp_htr_IAM_DB

mit

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SimpleHTR — Word-Level Handwritten Text Recognition

A word-recognition model that reads individual handwritten words from cropped images. It takes a single word image and predicts the text. Part of the Xournal++ HTR project.

Model details

Property	Value
Architecture	5 CNN layers + 2 bidirectional LSTM layers + CTC output
Input	Grayscale image, resized to 128×32 (uniform scale, centered)
Output	CTC log-probabilities (seq_len × num_classes)
Decoding	Greedy best-path
Format	ONNX (opset 17)
Parameters	~3.2M
Training data	IAM Handwriting Database
Best val CER	0.056 (5.6%)
Best val word accuracy	84.2%
License	MIT

Usage

python

from xournalpp_htr.inference_models import SimpleHTRModel

model = SimpleHTRModel.from_pretrained()
text = model.recognize(word_image_grayscale)  # str

Requires pip install xournalpp-htr (pulls onnxruntime and huggingface-hub, no PyTorch needed).

How it works

The model processes a grayscale word image through:

CNN encoder (5 layers): extracts visual features, reducing height to 1 and width to 32 timesteps via batch norm + ReLU + max pooling.
Bidirectional LSTM (2 layers, hidden=256): models sequential dependencies across timesteps.
CTC output layer: projects to character probabilities at each timestep.
Greedy decoding: collapses repeated characters and removes blanks.

Input images are resized with uniform scaling (preserving aspect ratio) and centered on a white 128×32 canvas, then normalised to [-0.5, 0.5].

Training

Trained on the IAM Handwriting Database (word-level, 95/5 random split). Best configuration found via grid search over 3 experiments:

Hyperparameter	Value
Optimizer	Adam
Learning rate	0.0005
Batch size	64
Dropout	0.5
Data augmentation	Enabled (blur, geometric, morphological, contrast)
Max epochs	200
Early stopping patience	25

Full training instructions and experiment logs: docs/models/simple_htr.md.

Intended use

This model is the recognition stage in a handwriting recognition pipeline, following a word detector that crops individual words. It is designed to run on personal devices (laptops, edge) via ONNX Runtime — no GPU required for inference.

Limitations

Single-word recognition only — expects a pre-cropped word image.
Greedy CTC decoding (no language model); beam search could improve results.
Grayscale input required.
Trained on IAM handwriting; performance on other styles may vary.
78-character charset (English letters, digits, punctuation) — does not support non-Latin scripts.

Citation

The architecture is based on SimpleHTR by Harald Scheidl.

PellelNitram/xournalpp-htr-simple-htr