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A Novel Scene Text Recognition Method Based on Deep Learning

Maosen Wang1, Shaozhang Niu1,*, Zhenguang Gao2

Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Department of Computer Science, Framingham State University, Framingham, MA 01772, USA.

* Corresponding Author: Shaozhang Niu. Email: email.

Computers, Materials & Continua 2019, 60(2), 781-794. https://doi.org/10.32604/cmc.2019.05595

Abstract

Scene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition task of sequence texts. Our model has the following three advantages in comparison to existing scene text recognition methods: First, the text recognition speed of the TRDD network is much fast than the state-of-the-art scene text recognition network based recurrent neural networks (RNN). Second, TRDD is easier to train, avoiding the problem of exploding and vanishing, which is major issue for RNN. Third, both using larger dilated factors and increasing the filter size are all viable ways to change receptive field size. We benchmark the TRDD on four standard datasets, it has higher recognition accuracy and faster recognition speed based on the smaller model. It is hopefully used in the real-time application.

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Cite This Article

M. Wang, S. Niu and Z. Gao, "A novel scene text recognition method based on deep learning," Computers, Materials & Continua, vol. 60, no.2, pp. 781–794, 2019.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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