A statistical–topological feature combination for recognition of handwritten numerals
Autor: | Mahantapas Kundu, Jagan Mohan Reddy, Mita Nasipuri, Nibaran Das, Dipak Kumar Basu, Subhadip Basu, Ram Sarkar |
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Rok vydání: | 2012 |
Předmět: |
business.industry
Computer science Speech recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Topology Telugu language.human_language Numeral system Support vector machine ComputingMethodologies_PATTERNRECOGNITION Bengali Principal component analysis language Artificial intelligence business Feature combination Classifier (UML) Software |
Zdroj: | Applied Soft Computing. 12:2486-2495 |
ISSN: | 1568-4946 |
DOI: | 10.1016/j.asoc.2012.03.039 |
Popis: | Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature. |
Databáze: | OpenAIRE |
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