19 January 2009 Script identification of handwritten word images
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This paper describes a system for script identification of handwritten word images. The system is divided into two main phases, training and testing. The training phase performs a moment based feature extraction on the training word images and generates their corresponding feature vectors. The testing phase extracts moment features from a test word image and classifies it into one of the candidate script classes using information from the trained feature vectors. Experiments are reported on handwritten word images from three scripts: Latin, Devanagari and Arabic. Three different classifiers are evaluated over a dataset consisting of 12000 word images in training set and 7942word images in testing set. Results show significant strength in the approach with all the classifiers having a consistent accuracy of over 97%.
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Anurag Bhardwaj, Anurag Bhardwaj, Huaigu Cao, Huaigu Cao, Venu Govindaraju, Venu Govindaraju, } "Script identification of handwritten word images", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470Z (19 January 2009); doi: 10.1117/12.805682; https://doi.org/10.1117/12.805682

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