Run demo

Go to the model/ directory and unzip the file model.zip (pre-trained on the IAM dataset). Take care that the unzipped files are placed directly into the model/ directory and not some subdirectory created by the unzip-program. Afterwards, go to the src/ directory and run python main.py. The input image and the expected output is shown below.

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

Command line arguments

• --train: train the NN, details see below.
• --validate: validate the NN, details see below.
• --beamsearch: use vanilla beam search decoding (better, but slower) instead of best path decoding.
• --wordbeamsearch: use word beam search decoding (only outputs words contained in a dictionary) instead of best path decoding. This is a custom TF operation and must be compiled from source, more information see corresponding section below. It should not be used when training the NN.
• --dump: dumps the output of the NN to CSV file(s) saved in the dump/ folder. Can be used as input for the CTCDecoder.

If neither --train nor --validate is specified, the NN infers the text from the test image (data/test.png). Two examples: if you want to infer using beam search, execute python main.py --beamsearch, while you have to execute python main.py --train --beamsearch if you want to train the NN and do the validation using beam search.

Train model

IAM dataset

The data-loader expects the IAM dataset [5] (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. Download ascii/words.txt.
4. Put words.txt into the data/ directory.
5. Create the directory data/words/.
6. Put the content (directories a01, a02, ...) of words.tgz into data/words/.
7. Go to data/ and run python checkDirs.py for a rough check if everything is ok.

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.