Scene text recognition using residual convolutional recurrent neural network
| Autor: | Hongmei Song, Sanyuan Zhao, Zhengchao Lei, Jianbing Shen |
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| Rok vydání: | 2018 |
| Předmět: |
Decodes
biology business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Text recognition biology.organism_classification Residual Convolutional neural network Computer Science Applications Recurrent neural network Hardware and Architecture Encoding (memory) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | Machine Vision and Applications. 29:861-871 |
| ISSN: | 1432-1769 0932-8092 |
| DOI: | 10.1007/s00138-018-0942-y |
| Popis: | Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. |
| Databáze: | OpenAIRE |
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