Abstract
Sparse Representation (SR) has successful applications in vision and classification problems. SR methods work by transforming the data into a low-rank latent space to find sparse representations for the data. Handwritten Character Recognition (HWCR) is a challenging classification task. Its difficulty arises from the high intraclass variances of the writing. HWCR for Latin scripts is better studied, but a few challenges remain. For Indic scripts, HWCR is not very mature and much research is needed. Indic scripts reflect the shallow orthography and are quite syllabic. Telugu is one such Indic script with more than 80 million native speakers. Literature available on Telugu HWCR is less. In this paper, we propose a framework for Telugu HWCR using sparse representation techniques focusing on confusion pairs. The proposed method is evaluated on confusion pairs from the standard HPL isolated Telugu character data set.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
