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Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time.

Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox.

Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures.

Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc.

Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.


Curvature Features Zonal Features Numeral Recognition Delta Distance Coding Slope Features

Article Details

How to Cite
Kumar Prasad, B. (2021). APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION. Students’ Research in Technology & Management, 9(2), 7-12.


  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Chakraborty, B., Shaw, B., Aich, J., Bhattacharya, U., Parui, S. (2018). Does deeper network lead to better accuracy: A case study on handwritten Devanagari characters. 13th IAPR international workshop on document analysis system (DAS), Vienna, Austria, IEEE.
  6. Chen, G., Li, Y., Srihari, S. (2016). Word recognition with deep conditional random fields. International Conference on Image Processing (ICIP), IEEE.
  7. Choudhury, A., Rana, H,S., Bhowmik, T. (2018). Handwritten Bengali numeral recognition using HoG based feature extraction algorithm. 5th international conference on signal processing and integrated networks (SPIN), Noida, India, IEEE.
  8. Dhande, P.S., Kharat, R. (2018). Character recognition for cursive English handwriting to recognize medicine name from doctor’s prescription. International conference on computing, communication, control and automation (ICCUBEA), Pune, India, IEEE.
  9. Karungaru, S., Terada, K., Fukumi, M. (2013). Hand Written Character Recognition using Star-Layered Histogram Features. SICE Annual Conference, Nagoya University, Japan, 1151-1155.
  10. LeCun, Y., Bottou, L., Bengio, Y. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  11. 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.
  12. 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.
  13. Qacimy, B., Kerroum, M., Hammouch, A. (2014). Feature extraction based on DCT for handwritten digit recognition, International Journal of Computer Science Issues. 11(2), 27-33.
  14. Xiao, X., Suen, C. 2012 (2012). A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4), 1318-1325.