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Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

Authors :
Abdelrahman Abdallah
Mohamed Hamada
Daniyar Nurseitov
Source :
Journal of Imaging, Vol 6, Iss 12, p 141 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

Details

Language :
English
ISSN :
2313433X
Volume :
6
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.be8899057ebb4fb4a17fe939df3af418
Document Type :
article
Full Text :
https://doi.org/10.3390/jimaging6120141