Determination of the Script and Language Content of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Script and Language Identification from Document Images
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Script Line Separation from Indian Multi-Script Documents
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Automatic script identification from images using cluster-based templates
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Page segmentation using texture analysis
Pattern Recognition
On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm
Engineering Applications of Artificial Intelligence
Off-line handwriting recognition system based on GA and visual encoding
Proceedings of the International Workshop on Multilingual OCR
Online handwriting recognition for the Arabic letter set
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
Text-Independent writer identification based on fusion of dynamic and static features
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords
Engineering Applications of Artificial Intelligence
Effect of delayed strokes on the recognition of online Farsi handwriting
Pattern Recognition Letters
Methodologies for recognition of old Slavic Cyrillic characters
International Journal of Computational Intelligence Studies
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Abstract--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: Arabic, Cyrillic, Devnagari, Han, Hebrew, or Roman. 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.