cermeng / document-image-binarization

A selectional auto-encoder approach for document image binarization

The code of this repository was used for the following publication. If you find this code useful please cite our paper:

@article{Gallego2017,
title = "A selectional auto-encoder approach for document image binarization",
author = "Jorge Calvo-Zaragoza and Antonio-Javier Gallego",
journal = "Pattern Recognition",
year = "2018"
}

Below we include instructions to binarize and reproduce the experiments.

Binarize

The binarize.py script performs the binarization of an input image using a trained model. The parameters of this script are the following:

Parameter	Default	Description
-imgpath		Path to the image to process
-modelpath		Path to the model to load
-w	256	Input window size
-s	-1	Step size. -1 to use window size
-f	64	Number of filters
-k	5	Kernel size
-drop	0	Dropout percentage
-stride	2	Convolution stride size
-every	1	Residual connections every x layers
-th	0.5	Selectional threshold
-save		Output image filename
--demo		Activate demo mode

The mandatory parameters are -imgpath and -modelpath, the rest are optional. You also have to choose if you want to see a demo (--demo) or to save (-save) the binarized image.

For example, to binarize the image img01.png using the model trained for Dibco 2016 and the parameters specified in the paper, you have to run the following command:

$ python binarize.py -imgpath img01.png -modelpath MODELS/model_weights_dibco_6_256x256_s96_aug_m205_f64_k5_s2_se3_e200_b32_esp.h5 -w 256 -s 96 -f 64 -k 5 -stride 2 -th 0.5 --demo

The MODELS folder includes the trained models for the datasets evaluated.

Train

The train.py script performs the training of the proposed network from a dataset of images and a series of input parameters that allow to configure both the training process and the network topology. This executable, in addition to training, performs the validation and saves the resulting images.

The parameters of this script are the following:

Parameter	Default	Description
-path		Base path to datasets
-db		Database name ['dibco','palm','phi','ein','sal','voy','bdi','all']
-dbp		Database dependent parameters [dibco fold, palm gt]
--aug		Load augmentation folders
-w	256	Input window size
-s	-1	Step size. -1 to use window size
-f	64	Number of filters
-k	5	Kernel size
-drop	0	Dropout percentage
-stride	2	Convolution stride size
-every	1	Residual connections every x layers
-th	0.5	Selectional threshold. -1 to evaluate from 0 to 1
-page	-1	Page size to divide the training set. -1 to load all
-start_from	0	Start from this page
-super	1	Number of super epochs
-e	200	Number of epochs
-b	10	Mini-batch size
-esmode	p	Early stopping mode: g='global', p='per page'
-espat	10	Early stopping patience
-verbose	1	1=show batch increment, other=mute
--test		Only run test (deactivate training)
--show		Show the output images
-loadmodel		Weights filename to load for test or for initialization

The only mandatory parameters are -path and -db. To run the training you must first indicate the path where the datasets are located (with the -path parameter) and the name of the dataset to evaluate (with the -db parameter). Depending on the name of the dataset the system will create the partitions for training and validation.

The folders of each dataset must have a specific name (see table in section "Datasets" or consult the source code). Within each folder there must be two subfolders, one with the suffix \_GR with the images in gray scale, and another with the suffix \_GT for the ground truth.

Option --aug activates the data augmentation, in this case the system will load the images stored in the folder with prefix aug_.

For example, to train a network for the dataset Dibco 2016, using data augmentation and the parameters specified in the paper, you have to run the following command:

$ python -u train.py -path datasets -db dibco -dbp 6 --aug -w 256 -s 128 -f 64 -k 5 -e 200 -b 10 -th -1 -stride 2 -page 64

Once the script finishes, it will save the learned weights and the binarized images resulting from the validation partition in a folder with the prefix _PR- followed by the name of the model. It will also show the mean squared reconstruction error and the obtained F-m. We have also used the evaluation tool provided by DIBCO (http://vc.ee.duth.gr/h-dibco2016/benchmark/), which allowed us to validate the obtained F-m and also calculate the pseudo F-m.

Datasets

Below is a summary table of the datasets used in this work along with a link from which they can be downloaded:

Dataset	Acronym	URL
DIBCO 2009	DB09	http://users.iit.demokritos.gr/~bgat/DIBCO2009/benchmark/
DIBCO 2010	DB10	http://users.iit.demokritos.gr/~bgat/H-DIBCO2010/benchmark/
DIBCO 2011	DB11	http://utopia.duth.gr/~ipratika/DIBCO2011/benchmark/
DIBCO 2012	DB12	http://utopia.duth.gr/~ipratika/HDIBCO2012/benchmark/
DIBCO 2013	DB13	http://utopia.duth.gr/~ipratika/DIBCO2013/benchmark/
DIBCO 2014	DB14	http://users.iit.demokritos.gr/~bgat/HDIBCO2014/benchmark/
DIBCO 2016	DB16	http://vc.ee.duth.gr/h-dibco2016/benchmark/
Palm Leaf	PL-I / PL-II	http://amadi.univ-lr.fr/ICFHR2016_Contest/
Persian docs	PHI	http://www.iapr-tc11.org/mediawiki/index.php/Persian_Heritage_Image_Binarization_Dataset_(PHIBD_2012)
Ensiedeln	ES	http://www.e-codices.unifr.ch/en/sbe/0611/
Salzinnes	SAM	https://cantus.simssa.ca/manuscript/133/