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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fangsheng Wu & Changan Zhu & Jinxiu Xu & Mohammed Wasim Bhatt & Ashutosh Sharma, 2022. "Research on image text recognition based on canny edge detection algorithm and k-means algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 72-80, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01262-0
    DOI: 10.1007/s13198-021-01262-0
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    References listed on IDEAS

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    1. Liping Liu & Lin Wang & Dan Xu & Hongjie Zhang & Ashutosh Sharma & Shailendra Tiwari & Manjit Kaur & Manju Khurana & Mohd Asif Shah, 2021. "CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, May.
    2. Hui Pang & Zheng Zheng & Tongmiao Zhen & Ashutosh Sharma, 2021. "Smart Farming: An Approach for Disease Detection Implementing IoT and Image Processing," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 12(1), pages 55-67, January.
    3. Di Fan & Xinyun Guo & Xiao Lu & Xiaoxin Liu & Bo Sun, 2020. "Image Defogging Algorithm Based on Sparse Representation," Complexity, Hindawi, vol. 2020, pages 1-8, July.
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    Cited by:

    1. Jibi G. Thanikkal & Ashwani Kumar Dubey & M. T. Thomas, 2023. "A novel edge detection method for medicinal plant's leaf features extraction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 448-458, February.
    2. Weiwei Sun & Huiqian Wang & Yi Lu & Jiasai Luo & Ting Liu & Jinzhao Lin & Yu Pang & Guo Zhang, 2022. "Deep-Learning-Based Complex Scene Text Detection Algorithm for Architectural Images," Mathematics, MDPI, vol. 10(20), pages 1-22, October.

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