Main Article Content
Abstract
Purpose of the study: This paper aims to recognition of handwritten English characters in offline mode. It develops an efficient character recognition model avoiding large variations in handwriting by using better feature extraction techniques.
Methodology: The samples of characters are preprocessed by applying a sequence of operations in succession like Thickening, Thresholding, Filtering, and Thinning. Efficient features like Gradient features and Zonal features have been extracted. Gradient features are helpful to find out stroke information in the character whereas Zonal features detail out local information in a more précised way. Hidden Markov Model is the classifier.
Main Findings: Classification has been started with only a 5-state HMM model but it is observed that as the number of states of HMM model is increased, the corresponding recognition rate is also improved. Finally, with the 36 states HMM model we have got the expected result. This produces an overall average recognition rate of 92.6%. For the letters ‘A’ and ‘W’, the recognition rate is found to be very low, because of a lot of variations in writing style of these letters.
Applications of this study: HMM is a flexible tool which is capable of absorbing variations in character images. The future works will be concentrated on improvement of recognition rate of such letters by finding some demarcating features and post processing. The proposed method can be well used in Natural Language Processing, Signature verification, Face recognition like other Pattern Recognition applications.
Novelty/Originality of this study: Preprocessing uses Median filter which removes all stray marks in samples and hence avoids any possibility of false pixels. The combination of Gradient features and Zonal features leads to a recognition accuracy of 92.6% which may be used by researchers in any other domains for the purpose of classification. The application of HMM will motivate the readers to use it for better results of classification.
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References
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- Dave, H., Patel, M.(2017). Handwritten English Character Recognition using Multilayer Perceptron Neural Network. IJARIIE, 3(3), 713-719.
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- Ding, K., Liu, Z.B., Jin, L.W., Zhu, X.H. (2007). A comparative study of Gabor Feature And Gradient Feature For Handwritten Chinese Character Recognition, ICWAPR, Beijing, China, 1182-1186.
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- Fujisawa, H., Liu, C.L. (2003). Directional Pattern Matching for Character Recognition, Proc. 7th ICDAR, Edinburgh, Scotland, 794-798.
- Liu, C.L., Nakashima, K., Sako., Fujisawa, H.(2004). Handwritten Digit Recognition: Investigation of Normalisation and Feature Extraction Techniques, Pattern Recognition, 37(2), 265-279. https://doi.org/10.1016/S0031-3203(03)00224-3 DOI: https://doi.org/10.1016/S0031-3203(03)00224-3
- Liu, H., Ding, X.(2005). Handwritten Character recognition Using Gradient feature and Quadratic Classifier with Multiple Discrimination Schemes, Proceedings of the Eighth International Conference on Document Analysis and Recognition(ICDAR).
- Mathur, A., Pathare, A., Sharma, P., Oak, S. (2019). AI based reading system for blind using OCR, International conference on electronics, communication and aerospace technology (ICECA), Coimbatore, India, IEEE. https://doi.org/10.1109/ICECA.2019.8822226 DOI: https://doi.org/10.1109/ICECA.2019.8822226
- Mokbel, C., Abi Akl H. and Greige H. (2002). Automatic speech recognition of Arabic digits over Telephone network, Proc.Research Trends in Science and Technology.
- Pal, A, Singh D.(2010). Handwritten English Character Recognition Using Neural Network, International Journal of Computer Science & Communication, 1(2), 141-144.
- Rabiner, L. R. (1998). A tutorial on Hidden Markov Models and selected applications in speech recognition, Proceedings of The IEEE, 77(2), 257-286. https://doi.org/10.1109/5.18626 DOI: https://doi.org/10.1109/5.18626
References
Celar, S., Stojkic, Z., Seremet, Z.(2015). Classification of Test Documents Based on Handwritten Student ID’s Characteristics. 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM, Procedia Engineering (Elsevier), 782 – 790. https://doi.org/10.1016/j.proeng.2015.01.432 DOI: https://doi.org/10.1016/j.proeng.2015.01.432
Chen, G., Li, Y., Srihari, S.(2016). Word recognition with deep conditional random fields. International Conference on Image Processing (ICIP), IEEE, 1928-1932. https://doi.org/10.1109/ICIP.2016.7532694 DOI: https://doi.org/10.1109/ICIP.2016.7532694
Dave, H., Patel, M.(2017). Handwritten English Character Recognition using Multilayer Perceptron Neural Network. IJARIIE, 3(3), 713-719.
Dhande P.S., Kharat, R.(2018). Character recognition for cursive English handwriting to recognize medicine me from doctor’s prescription, International conference on computing, communication, control and automation (ICCUBEA), Pune, India, IEEE. https://doi.org/10.1109/ICCUBEA.2017.8463842 DOI: https://doi.org/10.1109/ICCUBEA.2017.8463842
Ding, K., Liu, Z.B., Jin, L.W., Zhu, X.H. (2007). A comparative study of Gabor Feature And Gradient Feature For Handwritten Chinese Character Recognition, ICWAPR, Beijing, China, 1182-1186.
El Abed, H., Margner V.(2009). Arabic handwriting recognition competition. Inter. Journal on Document Analysis and Recognition, 9(2). https://doi.org/10.1109/ICDAR.2009.284 DOI: https://doi.org/10.1109/ICDAR.2009.256
El-Hajj, R., Likforman-Sulem, L., and Mokbel, C.(2005). Arabic Hand- writing Recognition Using Baseline Dependent Features and Hidden Markov Modeling, Proc. Eighth Intl Conf. Document Analysis and Recognition, 893-897. https://doi.org/10.1109/ICDAR.2005.53 DOI: https://doi.org/10.1109/ICDAR.2005.53
Fujisawa, H., Liu, C.L. (2003). Directional Pattern Matching for Character Recognition, Proc. 7th ICDAR, Edinburgh, Scotland, 794-798.
Liu, C.L., Nakashima, K., Sako., Fujisawa, H.(2004). Handwritten Digit Recognition: Investigation of Normalisation and Feature Extraction Techniques, Pattern Recognition, 37(2), 265-279. https://doi.org/10.1016/S0031-3203(03)00224-3 DOI: https://doi.org/10.1016/S0031-3203(03)00224-3
Liu, H., Ding, X.(2005). Handwritten Character recognition Using Gradient feature and Quadratic Classifier with Multiple Discrimination Schemes, Proceedings of the Eighth International Conference on Document Analysis and Recognition(ICDAR).
Mathur, A., Pathare, A., Sharma, P., Oak, S. (2019). AI based reading system for blind using OCR, International conference on electronics, communication and aerospace technology (ICECA), Coimbatore, India, IEEE. https://doi.org/10.1109/ICECA.2019.8822226 DOI: https://doi.org/10.1109/ICECA.2019.8822226
Mokbel, C., Abi Akl H. and Greige H. (2002). Automatic speech recognition of Arabic digits over Telephone network, Proc.Research Trends in Science and Technology.
Pal, A, Singh D.(2010). Handwritten English Character Recognition Using Neural Network, International Journal of Computer Science & Communication, 1(2), 141-144.
Rabiner, L. R. (1998). A tutorial on Hidden Markov Models and selected applications in speech recognition, Proceedings of The IEEE, 77(2), 257-286. https://doi.org/10.1109/5.18626 DOI: https://doi.org/10.1109/5.18626