A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition
AbstractScript identification is an essential step in document image processing especially when the environment is multi-script/multilingual. Till date researchers have developed several methods for the said problem. For this kind of complex pattern recognition problem, it is always difficult to decide which classifier would be the best choice. Moreover, it is also true that different classifiers offer complementary information about the patterns to be classified. Therefore, combining classifiers, in an intelligent way, can be beneficial compared to using any single classifier. Keeping these facts in mind, in this paper, information provided by one shape based and two texture based features are combined using classifier combination techniques for script recognition (word-level) purpose from the handwritten document images. CMATERdb8.4.1 contains 7200 handwritten word samples belonging to 12 Indic scripts (600 per script) and the database is made freely available at
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Mukhopadhyay, A.; Singh, P.K.; Sarkar, R.; Nasipuri, M. A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition. J. Imaging 2018, 4, 39.
Mukhopadhyay A, Singh PK, Sarkar R, Nasipuri M. A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition. Journal of Imaging. 2018; 4(2):39.Chicago/Turabian Style
Mukhopadhyay, Anirban; Singh, Pawan K.; Sarkar, Ram; Nasipuri, Mita. 2018. "A Study of Different Classifier Combination Approaches for Handwritten Indic Script Recognition." J. Imaging 4, no. 2: 39.
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