Research on image text recognition based on canny edge detection algorithm and k-means algorithm

Fangsheng Wu (), Changan Zhu (), Jinxiu Xu (), Mohammed Wasim Bhatt () and Ashutosh Sharma ()
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Fangsheng Wu: Anhui Business Vocational College
Changan Zhu: Anhui Business Vocational College
Jinxiu Xu: University of Science and Technology of China
Mohammed Wasim Bhatt: Central University of Punjab
Ashutosh Sharma: Southern Federal University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 8, 72-80

Abstract: Abstract The latest research in the field of recognition of image characters has led to various developments in the modern technological works for the improvement of recognition rate and precision. This technology is significant in the field of character recognition, business card recognition, document recognition, vehicle license plate recognition etc. for smart city planning, thus its effectiveness should be improved. In order to improve the accuracy of image text recognition effectively, this article uses canny algorithm to process edge detection of text, and k-means algorithm for cluster pixel recognition. This unique combination combined with maximally stable extremal region and optimization of stroke width for image text yields better results in terms of recognition rate, recall, precision, F-score and accuracy. The results show that the correct recognition rate is 88.3% and 72.4% respectively with an accuracy value of 90.5% for the proposed method. This algorithm has high image text recognition rate, can recognize images taken in complex environment, and has good noise removal function. It is significantly an optimal algorithm for image text recognition.

Keywords: Canny edge detection algorithm; k-means algorithm; Image text recognition; Noise removal (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-021-01262-0

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