Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. This Neural Network (NN) model recognizes the text contained in the images of segmented words. As these word-images are smaller than images of complete text-lines, the NN can be kept small and training on the CPU is feasible. 3/4 of the words from the validation-set are correctly recognized and the character error rate is around 10%.

Run demo

go to the src/ directory and run python web.py. Upload the input image and the expected output is received.

python main.py Validation character error rate of saved model: 10.624916% Init with stored values from ../model/snapshot-38 Recognized: " " Probability: 0.96625507

Tested with:

• Python 2 and Python 3
• TF 1.3, 1.10 and 1.12
• Ubuntu 16.04

The data-loader expects the IAM dataset (or any other dataset that is compatible with it) in the data/ directory. Follow these instructions to get the dataset:

1. Register for free at this website.
2. Download words/words.tgz.
3. Put the content (directories a01, a02, ...) of words.tgz into data/.

If you want to train the model from scratch, delete the files contained in the model/ directory. Otherwise, the parameters are loaded from the last model-snapshot before training begins. Then, go to the src/ directory and execute python main.py --train. After each epoch of training, validation is done on a validation set (the dataset is split into 95% of the samples used for training and 5% for validation as defined in the class DataLoader). If you only want to do validation given a trained NN, execute python main.py --validate.