Main Article Content
Purpose: Lines and Curves are important parts of characters in any script. Features based on lines and curves go a long way to characterize an individual character as well as differentiate similar-looking characters. The present paper proposes an English numerals recognition system using feature elements obtained from the novel and efficient coding of the curves and local slopes. The purpose of this paper is to recognize English numerals efficiently to develop a reliable Optical Character recognition system.
Methodology: K-Nearest Neighbour classification technique has been implemented on a global database MNIST to get an overall recognition accuracy rate of 96.7 %, which is competitive to other reported works in literature. Distance features and slope features are extracted from pre-processed images. The feature elements from training images are used to train K-Nearest-Neighbour classifier and those from test images have been used to classify them.
Main Findings: The findings of the current paper can be used in Optical Character Recognition (OCR) of alphanumeric characters of any language, automatic reading of amount on bank cheque, address written on envelops, etc.
Implications: Due to the similarity in structures of some numerals like 2, 3, and 8, the system produces respectively lower recognition accuracy rates for them.
Novelty: The ways of finding distance and slope features to differentiate the curves in the structure of English Numerals is the novelty of this work.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain the copyright without restrictions for their published content in this journal. IJSRTM is a SHERPA ROMEO Green Journal.
- Ashiquzzaman, A. & Tushar, A. K. (2017). Handwritten Arabic numeral recognition using deep learning neural networks. International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Dhaka, Bangladesh, IEEE. https://doi.org/10.1109/ICIVPR.2017.7890866
- Aziz, T., Rubel A.S., Salekin. S. & Kushol, R. (2018). Bangla handwritten numeral character recognition using directional pattern. 20th International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, IEEE. Computer and Information Technology (ICCIT), Dhaka, Bangladesh, IEEE.
- Babu, U., Venkateswarlu, Y. & Chintha, A. (2014). Handwritten Digit Recognition Using K-Nearest Neighbour Classifier. Proc. of World Congress on Computing and Communication Technologies, IEEE. https://doi.org/10.1109/WCCCT.2014.7
- Bhattacharya, U. & Chaudhuri, B.B. (2009). Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals. IEEE transactions on pattern analysis and machine intelligence, 31(3), 444 - 457. https://doi.org/10.1109/TPAMI.2008.88
- Celar, S., Stojkic, Z. & Seremet, Z. (2015). Classification of Test Documents Based on Handwritten Student ID’s Characteristics. 25th International Symposium on Intelligent Manufacturing and Automation (DAAAM), Procedia Engineering (Elsevier), 782 – 790. 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. https://doi.org/10.1109/ICIP.2016.7532694
- Karungaru, S., Terada, K. & Fukumi, M. (2013). Hand Written Character Recognition using Star-Layered Histogram Features. SICE Annual Conference, Nagoya University, Japan, 1151-1155.
- LeCun, Y., Bottou, L. & Bengio, Y. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
- Liu, C.L., Nakashima, K., Sako, H. & Fujisawa, H. (2004). Handwritten Digit Recognition: Investigation of Normalization and Feature Extraction Techniques. Pattern Recognition 37(2), 265-279. https://doi.org/10.1016/S0031-3203(03)00224-3
- Lopes, G., Silva, D., Rodrigues, A. & Filho, P. (2016). Recognition of handwritten digits using the signature features and Optimum Path Forest classifier. IEEE Latin America Transactions, 14, 2455-2460. https://doi.org/10.1109/TLA.2016.7530445
- Qacimy, B., Kerroum, M. & Hammouch, A. (2014). Feature extraction based on DCT for handwritten digit recognition. International Journal of Computer Science, 11(2), 27-33.
- Xiao, X. & Suen, C. (2012). A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4), 1318-1325. https://doi.org/10.1016/j.patcog.2011.09.021