A Comparative Study of Handwritten Character Recognition by using Image Processing and Neural Network Techniques

Abstract

This study aims to analyze the effects of noise, image filtering, and edge detection techniques in the preprocessing phase of character recognition by using a large set of character images exported from MNIST database trained with various sizes of neural networks. Canny edge detection algorithm was deployed to smooth the edges of the images while the Sobel edge detection algorithm was used to detect the edges of the images. Skeletonization algorithm was applied to re-shape the structural shapes. In the context of the image filtering, the Laplacian filter was utilized to enhance the images and High pass filtering was used to highlight the fine details in blurred images. Gaussian noise, image noise with Gaussian intensity, function in Matlab with the probability density function P was deployed on character images of MINST. Pattern recognition neural networks are widely used in optical character recognition. Feedforward neural networks are deployed in this study. A comprehensive analysis of the above-mentioned image processing techniques is included during character recognition. Improved accuracy is observed with character recognition during the prediction phase of the neural networks. A sample of unknown characters is tested with the application of High pass filtering + feedforward neural network and 89%, the highest, average output prediction accuracy was obtained. Other prediction accuracies were also tabulated for the reader’s attention.

Keywords

• Artificial intelligence (AI)
• edge detection
• feature extraction
• gradient
• hidden layer
• image correlation
• image filtering
• noise
• optical character recognition (OCR)
• pattern recognition

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References

1. [1] Y. Yin, W. Zhang, S. Hong, J. Yang, J. Xiong, and G. Gui, "Deep Learning-Aided OCR Techniques for Chinese Uppercase Characters in the Application of Internet of Things," in IEEE Access, vol. 7, pp. 47043-47049, 2019, DOI: 10.1109/ACCESS.2019.2909401
2. [2] I. Z. Yalniz and R. Manmatha, "Dependence Models for Searching Text in Document Images," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 1, pp. 49-63, 1 Jan. 2019, DOI: 10.1109/TPAMI.2017.2780108
3. [3] S. Porat, B. Carmeli, T. Domany, T. Drory, A. Geva, and A. Tarem, "Dynamic masking of application displays using OCR technologies," in IBM Journal of Research and Development, vol. 53, no. 6, pp. 10:1-10:14, Nov. 2009,DOI: 10.1147/JRD.2009.5429038
4. [4] Yihong Xu and G. Nagy, "Prototype extraction and adaptive OCR," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1280-1296, Dec. 1999, DOI: 10.1109/34.817408
5. [5] G. Vamvakas, B. Gatos, N. Stamatopoulos, and S. J. Perantonis, "A Complete Optical Character Recognition Methodology for Historical Documents," 2008 The Eighth IAPR International Workshop on Document Analysis Systems, Nara, 2008, pp. 525-532, DOI: 10.1109/DAS.2008.73
6. [6] G. Nikola, M. Dragan and G. Dejan, “System For Digital Processing, Storage and internet Publishing of Printed Textual Documents”, Fifth National Conference With International Participation ETAI'2000, pp. 21-23, 2000
7. [7] M. D. Kim and J. Ueda, "Dynamics-Based Motion Deblurring Improves the Performance of Optical Character Recognition During Fast Scanning of a Robotic Eye," in IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 491-495, Feb. 2018,DOI: 10.1109/TMECH.2018.2791473
8. [8] I. P. Morns and S. S. Dlay, "Analog design of a new neural network for optical character recognition," in IEEE Transactions on Neural Networks, vol. 10, no. 4, pp. 951-953, July 1999, DOI: 10.1109/72.774269

9. [9] K. A. Hamad, M. Kaya, “A Detailed Analysis of Optical Character Recognition Technology”, International Journal of Applied Mathematics, Electronics and Computers, Vol. 4, pp. 244-249, 2016
10. [10] M. D. Garris and D. L. Dimmick, "Form design for high accuracy optical character recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 653-656, June 1996, DOI: 10.1109/34.506417
11. [11] H. Singh and A. Sachan, "A Proposed Approach for Character Recognition Using Document Analysis with OCR," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 190-195, DOI: 10.1109/ICCONS.2018.8663011
12. [12] P. S. J. Kumar, “Adapted Optimal Neural Network Based Classifier Using Optical Character Recognition Engine for Tamil Language”, International Journal of Foundations of Computer Science, Vol. 5, pp. 30-37, 2015, DOI: 10.5121/ijfcst.2015.5404
13. [13] Sari Dewi Budiwati, J. Haryatno, and Eddy Muntina Dharma, "Japanese character (Kana) pattern recognition application using neural network," Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, Bandung, 2011, pp. 1-6, DOI: 10.1109/ICEEI.2011.6021648
14. [14] S. Pasha and M. C. Padma, "Recognition of handwritten Kannada characters using hybrid features," Fifth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2013), Bangalore, 2013, pp. 59-65, DOI: 10.1049/cp.2013.2238
15. [15] N. P. T. Kishna and S. Francis, "Intelligent tool for Malayalam cursive handwritten character recognition using artificial neural network and Hidden Markov Model," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 595-598, DOI: 10.1109/ICICI.2017.8365201
16. [16] S. A. Chaudhari and R. M. Gulati, "An OCR for separation and identification of mixed English — Gujarati digits using kNN classifier," 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), Gujarat, 2013, pp. 190-193, DOI: 10.1109/ISSP.2013.6526900
17. [17] J. Ghosn and Y. Bengio, "Bias learning, knowledge sharing," in IEEE Transactions on Neural Networks, vol. 14, no. 4, pp. 748-765, July 2003, DOI: 10.1109/TNN.2003.810608.