Abstract
In recent years, Optical Character Recognition(OCR) is widely used in machine vision. In this paper, we investigated the problem of optical character detection and recognition for Image-based in natural scene. The Optical Character Recognition is divided into three steps: (1) Selecting the candidate regions through image preprocessing. (2) The detection neural network is used to classify each region. The purpose is to retain text regions and remove non-text regions. (3) The recognition neural network is used to identify the characters in the text regions. We propose a novel algorithm. It integrates image preprocessing with Maximally Stable Extremal Regions(MSER), the neural network architecture of detection and the neural network architecture of recognition. Compared with previous works, the proposed algorithm has three distinctive properties: (1) We propose a new process of OCR algorithm. (2) The application scene of OCR algorithm is the images of natural scene. (3) The training data of recognition does not need artificial labels and can be generated indefinitely. Moreover, the algorithm has achieved good results in detection and recognition.
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Acknowledgment
Thanks to Institute of Department of information, Beijing University of Technology for supporting our work and giving us great suggestion. In addition Our work is supported by the national key research and development program (No. 2017YFC1703300) of China. At the same time, we also thank to the teachers and students who made great contribution to this study.
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Wang, B., Zhang, X., Cai, Y., Jia, M., Zhang, C. (2018). Optical Character Detection and Recognition for Image-Based in Natural Scene. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_39
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