Non-Redundant Contour Directional Feature Vectors for Character Recognition

Mishra, Tusar and Panda, Sandeep and Majhi, Banshidhar (2020) Non-Redundant Contour Directional Feature Vectors for Character Recognition. EAI Endorsed Transactions on Creative Technologies, 7 (25). p. 167204. ISSN 2409-9708

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This paper presents a novel shape based feature for printed character recognition. The shape features are derived from the contour of the character which is unique to all characters. Preprocessing is performed to standardize the characters and handle all variations such as bold, italics and bold-italics font characteristics. The complete character set is clustered into different groups based on contour feature. A probe character is mapped into the corresponding cluster prior to recognition. This helps to reduce the computational overhead. Finally two recognition schemes have been proposed, based on angle feature extracted from the contour information and a longest common substring (LCS) based feature. Simulation has been carried out to validate the efficacy of the proposed scheme on printed Odia characters. Performance accuracy has been compared with the existing schemes. In general, it is observed that the proposed scheme outperforms the competitive schemes.

Item Type: Article
Uncontrolled Keywords: OCR, Odia character recognition, pattern recognition, classification, feature extraction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 21 Jan 2021 06:55
Last Modified: 21 Jan 2021 06:55

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