MATLAB-Based App for Handwritten Numeral Recognition
Pages 331 - 337
Abstract
Handwritten numeral recognition (HNR) is widely used in various industries and is essential to the research and development of optical character recognition (OCR) technology. Given that most of the current HNR algorithms require high-quality input images rather than naturally taken photographs, a MATLAB-based application with a graphical user interface (GUI) is designed for pre-processing and segmenting photographs of handwritten numerals and then recognizing the numerals. For the various conditions that a naturally taken a photograph may have in practice, the application can perform denoising, perspective correction, and binarization of the original image, as well as removing horizontal lines, grids, or boxes from the background. The application can then segment and recognize every numeral from the processed image. It has been experimentally verified that the application can satisfactorily pre-process the original image while providing high accuracy in recognizing numerals.
References
[1]
Yanfeng Fan, Le Xiao and Tong Zhen. 2005. Research on Recognition Technology of Freely Handwritten Numerals. In Journal of Computer Engineering. (10). 168-170.
[2]
Xinzhong Zhang. 1992. Recognition Technology of Chinese Characters. Tsinghua University Press, Beijing. 1-8.
[3]
Binbin Chen. 2006. High-precision Recognition of Handwritten Numerals. Beijing University of Posts and Telecommunications, Beijing.
[4]
Rong Lü. 2006. Information Entry and Processing System Based on Handwritten Digit Recognition. Shandong University, Jinan, Shandong.
[5]
Jia Luo. 2007. Segmentation and Recognition of Unconstrained Handwritten Numeral Strings. Sichuan Normal University, Chengdu, Sichuan.
[6]
Martin T. Hagan, Howard B. Demuth and Markk H. Beale. 2002. Neural Network Design (Chinese Ed.). Machinery Industry Press, Beijing.
[7]
V. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
[8]
C. Cortes and V. Vapnik. 1995. Support Vector Networks. In Machine Learning. (20). 273-297.
[9]
Meicheng Chen. 2008. Recognition of Handwritten Numerals Based on Multi-neural Network Integration. Southwestern University of Finance and Economics, Chengdu, Sichuan.
[10]
Tingna Du. 2007. Spatial Transformation of Images and Its Implementation in MATLAB. In Journal of Anshan University of Science and Technology. (04). 368-371+376.
[11]
Guangming Liang, Donghua Liu, Bo Li 2002. Improvement of 2-Dimensional Otsu Adaptive Threshold Segmentation Algorithm. In Automation Technology and Applications. (21). 43-47.
[12]
Simon Haykin and Bart Kosko. 2001. GradientBased Learning Applied to Document Recognition. In Intelligent Signal Processing. 306-351.
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Published In
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
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Association for Computing Machinery
New York, NY, United States
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Published: 15 March 2023
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EITCE 2022
EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 21 - 23, 2022
Xiamen, China
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