OCR with a convolutional neural networks integration model in machine vision

Rui Zhang; Xiaojun Wu; Lingteng Qiu; Zhicheng Yang

doi:10.1117/12.2503305

9 August 2018 OCR with a convolutional neural networks integration model in machine vision

Rui Zhang, Xiaojun Wu, Lingteng Qiu, Zhicheng Yang

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062X (2018) https://doi.org/10.1117/12.2503305
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Optical character recognition (OCR) in complex scenes, particularly in industry environment, is a challenging problem that has received a significant amount of attention. A unified model for different types of character in different production lines is needed. In this paper, we propose a unified framework to classify characters using convolutional neural network (CNN) to satisfy the two main requirements in industrial OCR, the high recognition rate and less training time by combining the representational power of multi-layer neural networks together with multi-stage features. In the model, there are three CNNs, two with multi-stage features and one with deeper layers, which can be used to extract different fonts and types characters in different complex background. The results in experiments demonstrate the efficiency with high recognition rate and less training time in complex industrial environment.

Citation Download Citation

Rui Zhang, Xiaojun Wu, Lingteng Qiu, and Zhicheng Yang "OCR with a convolutional neural networks integration model in machine vision", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062X (9 August 2018); https://doi.org/10.1117/12.2503305

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