Semantic text segmentation from synthetic images of full-text documents

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43958267" target="_blank" >RIV/49777513:23520/19:43958267 - isvavai.cz</a>

Result on the web

<a href="http://proceedings.spiiras.nw.ru/index.php/sp/article/view/4527/2627" target="_blank" >http://proceedings.spiiras.nw.ru/index.php/sp/article/view/4527/2627</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15622/sp.2019.18.6.1381-1406" target="_blank" >10.15622/sp.2019.18.6.1381-1406</a>

Result language

angličtina

Original language name

Semantic text segmentation from synthetic images of full-text documents

Original language description

An algorithm (divided into multiple modules) for generating images of fulltext documents is presented. These images can be used to train, test, and evaluate models for Optical Character Recognition (OCR). The algorithm is modular, individual parts can be changed and tweaked to generate desired images. A method for obtaining background images of paper from already digitized documents is described. For this, a novel approach based on Variational AutoEncoder (VAE) to train a generative model was used. These backgrounds enable the generation of similar background images as the training ones on the fly. The module for printing the text uses large text corpora, a font, and suitable positional and brightness character noise to obtain believable results (for natural-looking aged documents). A few types of layouts of the page are supported. The system generates a detailed, structured annotation of the synthesized image. Tesseract OCR to compare the real-world images to generated images is used. The recognition rate is very similar, indicating the proper appearance of the synthetic images. Moreover, the errors which were made by the OCR system in both cases are very similar. From the generated images, fully-convolutional encoder-decoder neural network architecture for semantic segmentation of individual characters was trained. With this architecture, the recognition accuracy of 99.28% on a test set of synthetic documents is reached.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SPIIRAS Proceedings

ISSN

2078-9181

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

6

Country of publishing house

RU - RUSSIAN FEDERATION

Number of pages

26

Pages from-to

1380-1405

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85078454715