Code for the paper STN-OCR: A single Neural Network for Text Detection and Text Recognition

STN-OCR: A single Neural Network for Text Detection and Text Recognition

This repository contains the code for the paper: STN-OCR: A single Neural Network for Text Detection and Text Recognition

Please note that we refined our approach and released new source code. You can find the code here

Please use the new code, if you want to experiment with FSNS like data and our approach. It should also be easy to redo the text recognition experiments with the new code, although we did not release any code for that.

Structure of the repository

The folder datasets contains code related to datasets used in the paper. datasets/svhn contains several scripts that can be used to create svhn based ground truth files as used in our experiments reported in section 4.2., please see the readme in this folder on how to use the scripts. datasets/fsns contains scripts that can be used to first download the fsns dataset, second extract the images from the downloaded files and third restructure the contained gt files.

The folder mxnet contains all code used for training our networks.

Installation

In order to use the code you will need the following software environment:

Install python3 (the code might work with python2, too, but this is untested)
it might be a good idea to use a virtualenv
install all requirements with pip install -r requirements.txt
clone and install warp-ctc from here
go into the folder mxnet/metrics/ctc and run python setup.py build_ext --inplace
clone the mxnet repository
checkout the tag v0.9.3
add the warpctc plugin to the project by enabling it in the file config.mk
compile mxnet
install the python bindings of mxnet
You should be ready to go!

Training

You can use this code to train models for three different tasks.

SVHN House Number Recognition

The file train_svhn.py is the entry point for training a network using our purpose build svhn datasets. The file as such is ready to train a network capable of finding a single house number placed randomly on an image.

Example: centered_image

In order to do this, you need to follow these steps:

Download the datasets
Locate the folder generated/centered
open train.csv and adapt the paths of all images to the path on your machine (do the same with valid.csv)
make sure to prepare your environment as described in installation
start the training by issuing the following command:

python train_svhn.py --gpus --log-dir -b --lr 1e-5 --zoom 0.5 --char-map datasets/svhn/svhn_char_map.json
Wait and enjoy.

If you want to do experiments on more challenging images you might need to update some parts of the code in train_svhn.py. The parts you might want to update are located around line 40 in this file. Here you can change the max. number of house numbers in the image (num_timesteps), the maximum number of characters per house number (labels_per_timestep), the number of rnn layers to use for predicting the localization num_rnn_layers and whether to use a blstm for predicting the localization or not use_blstm.

A quite more challenging dataset is contained in the folder medium_two_digits, or medium in the datasets folder. Example: 2_digits_more_challenge

If you want to follow our experiments with svhn numbers placed in a regular grid you'll need to do the following:

Download the datasets
Locate the folder generated/easy
open train.csv and adapt the paths of all images to the path on your machine (do the same with valid.csv)
set num_timesteps and labels_per_timestep to 4 in train_svhn.py
start the training using the following command: python train_svhn.py --gpus --log-dir -b --lr 1e-5
If you are lucky it will work ;)

Text Recognition

Following our text recognition experiments might be a little difficult, because we can not offer the entire dataset used by us. But it is possible to perform the experiments based on the Synth-90k dataset provided by Jaderberg et al. here. After downloading and extracting this file you'll need to adapt the groundtruth file provided with this dataset to fit to the format used by our code. Our format is quite easy. You need to create a csv file with tabular separated values. The first column is the absolute path to the image and the rest of the line are the labels corresponding to this image.

To train the network you can use the train_text_recognition.py script. You can start this script in a similar manner to the train_svhn.py script.

FSNS

In order to redo our experiments on the FSNS dataset you need to perform the following steps:

Download the fsns dataset using the download_fsns.py script located in datasets/fsns
Extract the individual images using the tfrecord_to_image.py script located in datasets/fsns/tfrecord_utils (you will need to install tensorflow for doing that)
Use the transform_gt.py script to transform the original fsns groundtruth, which is based on a single line to a groundtruth containing labels for each word individually. A possible usage of the transform_gt.py script could look like this:

python transform_gt.py datasets/fsns/fsns_char_map.json
Because MXNet expects the blank label to be 0 for the training with CTC Loss, you have to use the swap_classes.py script in datasets/fsns and swap the class for space and blank in the gt, by issuing:

python swap_classes.py 0 133
After performing these steps you should be able to run the training by issuing:

python train_fsns.py --char-map datases/fsns/fsns_char_map.json --blank-label 0

Observing the Training Progress

We've added a nice script that makes it possible to see how well the network performs at every step of the training. This progress is normally plotted to disk for each iteration and can later on be used to create animations of the train progress (you can use the create_gif.py and create_video.py scripts located in mxnet/utils for this purpose). Besides this normal plotting to disk it is also possible to directly see this progress while the training is running. In order to see this you have to do the following:

start the show_progress.py script in mxnet/utils
start the training with the following additional command line params:

--send-bboxes --ip --port \t
run the chosen evaluation script like so

python eval__model.py /

You can use eval_svhn_model.py for evaluating a model trained with CTC on the original svhn dataset, the eval_text_recognition_model.py script for evaluating a model trained for text recognition, and the eval_fsns_model.py for evaluating a model trained on the FSNS dataset.

License

This Code is licensed under the GPLv3 license. Please see further details in LICENSE.md.

Citation

If you are using this Code please cite the following publication:

@article{bartz2017stn,
  title={STN-OCR: A single Neural Network for Text Detection and Text Recognition},
  author={Bartz, Christian and Yang, Haojin and Meinel, Christoph},
  journal={arXiv preprint arXiv:1707.08831},
  year={2017}
}

A short note on code quality

The code contains a huge amount of workarounds around MXNet, as we were not able to find any easier way to do what we wanted to do. If you know a better way, pease let us know, as we would like to have code that is better understandable, as now.

stn-ocr