keras-htr 0.1.0.post1

A toolkit for developing handwritten text recognition (HTR) pipelines

A toolkit for developing handwritten text recognition (HTR) pipelines

Easy to use toolkit for the rapid development of offline handwritten text recognition (HTR) system. The toolkit provides a set of useful utilities and scripts for training and evaluating models and performing recognition. It is shipped with ready-to-train model implementations for HTR tasks.

Key features

• built-in model implementations
• automatic data pre-processing
• built-in performance metrics: LER (label error rate)
• data-set independence
• handwriting language independence

Built-in models

• 1D-LSTM [1] by Joan Puigcerver

Quick start

Create working HTR system in just 4 steps:

• Subclass Source class representing raw data examples
• Use the data source to build a dataset
• Train model with a particular architecture on the dataset
• Take trained model and use it to perform recognition

You only need to focus on the first step. Once you implement a class for a data source, the steps that follow will automatically pre-process the data, train a neural network and save it.

Below is example of training 1D-LSTM model on synthetic images using SyntheticSource class.

Build a dataset using synthetic words data source, store it in temp_ds folder

python build_lines_dataset.py --source='keras_htr.data_source.synthetic.SyntheticSource' --destination=temp_ds

Note that the source argument expects a fully-qualified name of a class representing a data source.

Train a model

python train.py temp_ds --units=32 --epochs=35 --model_path=conv_lstm_model.h5

Run demo script

python demo.py conv_lstm_model.h5 temp_ds/test

Recognize handwriting

python htr.py conv_lstm_model.h5 temp_ds/character_table.txt temp_ds/test/0.png

Data sources

A data source is a Python generator that yields raw examples in the form of tuples (text, line_image). The Keras-HTR toolkit uses data sources to construct a train/val/test split, build a character table, collect useful meta-information about the data set such as average image height, width and more.

To train a model on your data, you need to create your subclass of Source class and implement an iterator method that yields a pair (line_image, text) at each step. Here line_image is either a path to an image file or Pillow image object, the text is a corresponding transcription.

SyntheticSource

It is generator of printed text examples

IAMSource

It is generator of handwritings taken from IAM handwriting database. Before you can use this source, you have to download the actual database: http://www.fki.inf.unibe.ch/databases/iam-handwriting-database.

References

[1] Joan Puigcerver. Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?