[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5573/IEIESPC.2017.6.1.053

Convolutional Neural Networks for Character-level Classification

Ko, Dae-Gun (Imaging Lab, Samsung S-Printing Solution Co., LTD.)
Song, Su-Han (Imaging Lab, Samsung S-Printing Solution Co., LTD.)
Kang, Ki-Min (Imaging Lab, Samsung S-Printing Solution Co., LTD.)
Han, Seong-Wook (Imaging Lab, Samsung S-Printing Solution Co., LTD.)

Publication Information

IEIE Transactions on Smart Processing and Computing / v.6, no.1, 2017 , pp. 53-59 More about this Journal

Abstract

Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper- and lower-case characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

Keywords

OCR; Deep-Learning; Convolutional neural networks; MNIST;

Citations & Related Records

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