Reference TypeConference Proceedings
Citation KeySouzaNetoBezeToseLima:2020:DeLeSy
Author1 Souza Neto, Arthur Flor de
2 Bezerra, Byron Leite Dantas
3 Toselli, Alejandro Hector
4 Lima, Estanislau Baptista
Affiliation1 Universidade de Pernambuco
2 Universidade de Pernambuco
3 Universitat Politecnica de Valencia
4 Universidade de Pernambuco
TitleHTR-Flor: a deep learning system for offline handwritten text recognition
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
Book TitleProceedings
DateNov. 7-10, 2020
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationVirtual
KeywordsHandwritten Text Recognition, Gated Convolutional Neural Networks, Gated CNN, Deep Neural Networks.
AbstractIn recent years, Handwritten Text Recognition (HTR) has captured a lot of attention among the researchers of the computer vision community. Current state-of-the-art approaches for offline HTR are based on Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, deep models such as CRNNs, Recurrent Neural Networks (RNNs) are likely to suffer from vanishing/exploding gradient problems when processing long text images, which are commonly found in scanned documents. Besides, they usually have millions of parameters which require huge amount of data, and computational resource. Recently, a new class of neural network architecture, called Gated Convolutional Neural Networks (Gated-CNN), has demonstrated potentials to complement CRNN methods in modeling. Therefore, in this paper, we present a new architecture for HTR, based on Gated-CNN, with fewer parameters and fewer layers, which is able to outperform the current state-of-the-art architectures for HTR. The experiment validates that the proposed model has statistically significant recognition results, surpassing previous HTR systems by an average of 33% over five important handwritten benchmark datasets. Moreover, the proposed model is able to achieve satisfactory recognition rates even in case of few training data. Finally, its compact architecture requires less computational resources, which can be applied for real-world applications that have hardware limitations, such as robots and smartphones.
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