Title: Hybrid model for Chinese character recognition based on Tesseract-OCR

Authors: Bo Wang; Yi-Wei Ma; Hong-Tao Hu

Addresses: Logistics Engineering Collage, Shanghai Maritime University, Pudong New Area, Shanghai City, China ' Department of Electrical Engineering, National Taiwan University of Science and Technology, Da'an District, Taipei City, Taiwan; China Institute of FTZ Supply Chain, Shanghai Maritime University, Pudong New Area, Shanghai City, China ' Logistics Engineering Collage, Shanghai Maritime University, Pudong New Area, Shanghai City, China

Abstract: Optical character recognition (OCR) is an important way to input information into a computer. And text information can be extracted by OCR from an image. Currently, the accuracy rate of Chinese OCR can also be improved. This study proposes a hybrid Chinese character recognition model based on the characteristics of Chinese. Before the OCR engine works, the model first filters the interference information in the image. Then the model adjusts the aspect ratio of the character. After an image is identified by OCR, single character recognition result is obtained. Then the result is checked and corrected on the phrase level. The experimental results show that the hybrid model improves the accuracy rate of Chinese OCR. Through image processing, the correct rate of recognition by the Tesseract-OCR engine is increased by about 12%, and the natural language processing improves the accuracy of the recognition result by about 5%.

Keywords: hybrid model; image processing; Chinese character; optical character recognition; OCR; phrase processing; K-nearest neighbour; KNN; Tesseract-OCR; single char recognition.

DOI: 10.1504/IJIPT.2020.106316

International Journal of Internet Protocol Technology, 2020 Vol.13 No.2, pp.102 - 108

Received: 06 Oct 2018
Accepted: 27 Feb 2019
Published online: 02 Apr 2020 *

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