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A deep learning toolkit specialized for handwritten document analysis
| Date | Stars |
|---|---|
| 2026-07-31 | 259 |
| 2026-08-02 | 259 |
| 2026-08-06 | 260 |
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# PyLaia
**PyLaia is a device agnostic, PyTorch based, deep learning toolkit for handwritten document analysis.**
**It is also a successor to [Laia](https://github.com/jpuigcerver/Laia).**
**Development/Maintenance is now done on [Teklia's fork](https://gitlab.teklia.com/atr/pylaia). Please head over there if you want to use the latest version of PyLaia.**
[](https://github.com/jpuigcerver/PyLaia/actions?query=workflow%3A%22Laia+CI%22)
[](https://codecov.io/gh/jpuigcerver/PyLaia)
[](https://www.codefactor.io/repository/github/jpuigcerver/PyLaia)
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://github.com/pre-commit/pre-commit)
[](https://github.com/ambv/black)
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Get started by having a look at our [Wiki](https://github.com/jpuigcerver/PyLaia/wiki)!
###### Several (mostly undocumented) examples of its use are provided at [PyLaia-examples](https://github.com/carmocca/PyLaia-examples).
## Installation
In order to install PyLaia, follow this recipe:
```bash
git clone https://github.com/jpuigcerver/PyLaia
cd PyLaia
pip install -e .
```
Please note that the CUDA version of nnutils ([`nnutils-pytorch-cuda`](https://pypi.org/project/nnutils-pytorch-cuda/)) is installed by default. If you do not have a GPU, you should install the CPU version ([`nnutils-pytorch`](https://pypi.org/project/nnutils-pytorch/)).
The following Python scripts will be installed in your system:
- [`pylaia-htr-create-model`](laia/scripts/htr/create_model.py): Create a VGG-like model with BLSTMs on top for handwriting text recognition. The script has different options to customize the model. The architecture is based on the paper ["Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?"](https://ieeexplore.ieee.org/document/8269951) (2017) by J. Puigcerver.
- [`pylaia-htr-train-ctc`](laia/scripts/htr/train_ctc.py): Train a model using the CTC algorithm and a set of text-line images and their transcripts.
- [`pylaia-htr-decode-ctc`](laia/scripts/htr/decode_ctc.py): Decode text line images using a trained model and the CTC algorithm. It can also output the char/word segmentation boundaries of the symbols recognized.
- [`pylaia-htr-netout`](laia/scripts/htr/netout.py): Dump the output of the model for a set of text-line images in order to decode using an external language model.
## Acknowledgments
Work in this toolkit was financially supported by the [Pattern Recognition and Human Language Technology (PRHLT) Research Center](https://www.prhlt.upv.es/wp/)
## BibTeX
```
@misc{puigcerver2018pylaia,
author = {Joan Puigcerver and Carlos Mocholí},
title = {PyLaia},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jpuigcerver/PyLaia}},
commit = {commit SHA}
}
```
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matched fp:81f6b27907d78e6c, llm:Description: 'A deep learning toolkit specialized for handwritten document analysis' (repository focuses on deep learning for handwriting/document analysis).
matched fp:81f6b27907d78e6c, llm:Description: 'A deep learning toolkit specialized for handwritten document analysis' (repository focuses on deep learning for handwriting/document analysis).
matched fp:81f6b27907d78e6c, llm:Description: 'A deep learning toolkit specialized for handwritten document analysis' (repository focuses on deep learning for handwriting/document analysis).