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Issue No.01 - January (2004 vol.26)
pp: 124-130
ABSTRACT
<p><b>Abstract</b>—Automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents and search for documents on the Web containing a particular script. The increase in usage of handheld devices which accept handwritten input has created a growing demand for algorithms that can efficiently analyze and retrieve handwritten data. This paper proposes a method to classify words and lines in an online handwritten document into one of the six major scripts: <it>Arabic, Cyrillic, Devnagari, Han, Hebrew, or Roman</it>. The classification is based on 11 different spatial and temporal features extracted from the strokes of the words. The proposed system attains an overall classification accuracy of 87.1 percent at the word level with 5-fold cross validation on a data set containing 13,379 words. The classification accuracy improves to 95 percent as the number of words in the test sample is increased to five, and to 95.5 percent for complete text lines consisting of an average of seven words.</p>
INDEX TERMS
Document understanding, handwritten script identification, online document, evidence accumulation, feature design.
CITATION
Anoop M. Namboodiri, Anil K. Jain, "Online Handwritten Script Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.26, no. 1, pp. 124-130, January 2004, doi:10.1109/TPAMI.2004.10009
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