APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION
| Autor: | Binod Kumar Prasad |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Feature vector English numerals Feature extraction Image processing Pattern recognition 02 engineering and technology Optical character recognition computer.software_genre 01 natural sciences Facial recognition system Numeral system 0103 physical sciences Word recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 010306 general physics business computer |
| Zdroj: | International Journal of Students' Research in Technology & Management. 9:7-12 |
| ISSN: | 2321-2543 |
| DOI: | 10.18510/ijsrtm.2021.922 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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