Offline Handwritten English Alphabet Recognition (OHEAR)
Keywords:Alphabet Recognition, Handwriting Recognition, Multi-Class Support Vector Machine, Feature Extraction, Optical Character Recognition
In most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%.
J. Mantas. “An overview of character recognition methodologies”. Pattern Recognition, vol. 19, no. 6, pp. 425-430, 1986.
D. Sinwar, V. S. Dhaka, N. Pradhan and S. Pandey. “Offline script recognition from handwritten and printed multilingual documents: A survey”. International Journal on Document Analysis and Recognition, vol. 24, no. 1-2, pp. 97-121, 2021.
D. Ghosh and A. P. Shivaprasad. “Handwritten script identification using the possibilistic approach for cluster analysis”. Journal of the Indian Institute of Science, vol. 80,no. 3, pp. 215, 2000.
U. Pal. “Automatic script identification: A survey”. J. VIVEK, Bombay, vol. 16, no. 3, pp. 2635, 2006.
K. Ubul, G. Tursun, A. Aysa, D. Impedovo, G. Pirlo and I. Yibulayin. “Script Identification of Multi-Script Documents: A Survey”. IEEE Access, vol. ???, p. 1, 2017.
A. Priya, S. Mishra, S. Raj, S. Mandal and S. Datta. “Online and offline character recognition: A survey”. 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0967-0970, 2016.
X. Y. Zhang, Y. Bengio and C. L. Liu. “Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark”. Pattern Recognition, vol. 61, pp. 348-360, 2017.
B. M. Vinjit, M. K. Bhojak, S. Kumar and G. Chalak. “A Review on Handwritten Character Recognition Methods and Techniques”. In: 2020 International Conference on Communication and Signal Processing (ICCSP), 2020
C. I. Patel, R. Patel and P. Patel. “Handwritten character recognition using neural network,” International Journal of Scientific and Engineering Research. vol. 2, no. 5, pp. 1-6, 2011.
A. Gupta, M. Srivastava and C. Mahanta. “Offline handwritten character recognition using neural network”. In: 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE), 2011.
M. Karthi, R. Priscilla and K. S. Jafer. “A novel content detection approach for handwritten English letters”. Procedia Computer Science. vol. 172, pp. 1016-1025, 2020.
B. Ibrahim, H. Yaseen and R. Sarhan. “English character recognition system using hybrid classifier based on MLP AND SVM”. International Journal of Inventions in Engineering and Science Technology, vol. 5, pp. 1-15, 2019.
S. Parkhedkar, S. Vairagade, V. Sakharkar, B. Khurpe, A. Pikalmunde, A. Meshram and R. Jambhulkar. “Handwritten English character recognition and translate English to Devnagari words”. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. ???, pp. 142-151, 2019.
N. Gautam and S. S. Chai. “Zig-zag diagonal and ANN for English character recognition”. International Journal of Advanced Research in Computer Science. vol. 8, no. 1-4, pp. 57-62, 2019.
S. R. Zanwar, A. S. Narote and S. P. Narote. “English character recognition using robust back propagation neural network”. In: Communications in Computer and Information Science. Springer, Singapore, pp. 216-227, 2019.
S. R. Zanwar, U. B. Shinde, A. S. Narote and S. P. Narote. “Handwritten English character recognition using swarm intelligence and neural network”. In: Intelligent Systems, Technologies and Applications. Springer, Singapore, pp. 93-102, 2020.
H. Freeman. “Computer processing of line-drawing images”. ACM Computing Surveys, vol. 6, no. 1, pp. 57-97, 1974.
V. N. Vapnik and V. Vapnik. “Statistical Learning Theory”. Vol. 1. Wiley, New York, 1998.
N. Cristianini and J. Shawe-Taylor. “Background Mathematics”. In: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, pp. 165-172, 2013.
AQ5???. “Learning with kernels: Support vector machines, regularization, optimization, and beyond,” IEEE Transactions on Neural Networks and Learning Systems, vol. 16, no. 3, pp. 781-781, 2005.
Y. Liu and H. Zhou. “MSVM recognition model for dynamic process abnormal pattern based on multi-kernel functions”. Journal of Systems Science and Information, vol. 2, no. 5, pp. 473-480, 2014.
Copyright (c) 2022 Hamsa D. Majeed, Goran Saman Nariman
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.