Topic
Devanagari
About: Devanagari is a research topic. Over the lifetime, 655 publications have been published within this topic receiving 7428 citations. The topic is also known as: Deva nagari & Hindi Script.
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Proceedings Article•
23 Oct 2006
TL;DR: A system for recognition of online handwritten characters has been presented for Indian writing systems and the results have been presented after testing the system on Devanagari and Telugu scripts.
Abstract: A system for recognition of online handwritten characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classied. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Devanagari and Telugu scripts.
99 citations
TL;DR: An efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones to reduce the number of distinct component classes compared to the total number of classes in Indic scripts is proposed.
97 citations
TL;DR: The results support a partly phonemic and partly syllabic level of segmentation, consistent with the structural hybridity of the script.
96 citations
TL;DR: A general fuzzy hyperline segment neural network is proposed that combines supervised and unsupervised learning in a single algorithm so that it can be used for pure classification, pure clustering and hybrid classification/clustering.
94 citations
01 Sep 2000
TL;DR: A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted and the use of writer dependent models to improve the recognition accuracy is explored.
Abstract: Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, directional and shape of the constituent strokes. An online pen computing environment has numerous application in providing an easy human interface for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local online properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We also explore the use of writer dependent models to improve the recognition accuracy.
90 citations