64 research outputs found
Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition
In handwritten character recognition, benchmark database plays an important
role in evaluating the performance of various algorithms and the results
obtained by various researchers. In Devnagari script, there is lack of such
official benchmark. This paper focuses on the generation of offline benchmark
database for Devnagari handwritten numerals and characters. The present work
generated 5137 and 20305 isolated samples for numeral and character database,
respectively, from 750 writers of all ages, sex, education, and profession. The
offline sample images are stored in TIFF image format as it occupies less
memory. Also, the data is presented in binary level so that memory requirement
is further reduced. It will facilitate research on handwriting recognition of
Devnagari script through free access to the researchers.Comment: 5 pages, 8 figures, journal pape
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
Zone Segmentation and Thinning based Algorithm for Segmentation of Devnagari Text
Character segmentation of handwritten documents is an challenging research topic due to its diverse application environment.OCR can be used for automated processing and handling of forms, old corrupted reports, bank cheques, postal codes and structures. Now Segmentation of a word into characters is one of the major challenge in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document and the next hurdle is presence of broken ,touching and overlapped characters in devnagari script. So, in this paper we have introduced an algorithm that will segment both broken as well as touching characters in devnagari script. Now to segment these characters the algorithm uses both zone segmentation and thinning based techniques. We have used 85 words each for isolated, broken, touching and both broken as well as touching characters individually. Results achieved while segmentation of broken as well as touching are 96.2 % on an average
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