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dc.contributor.authorBhunia A.K.-
dc.contributor.authorDas A.-
dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorPal U.-
dc.date.accessioned2020-12-02T11:41:27Z-
dc.date.available2020-12-02T11:41:27Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the International Conference on Document Analysis and Recognition, ICDAR, (2015), 636- 640-
dc.identifier.isbn9.78148E+12-
dc.identifier.issn15205363-
dc.identifier.urihttps://doi.org/10.1109/ICDAR.2015.7333839-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/15615-
dc.description.abstractRecognition of Bangla handwritten text is difficult due to its complex nature of having modifiers and headlines features. This paper presents a comparative study of different features namely LGH (Local Gradient of Histogram), PHOG (Pyramid Histogram of Oriented Gradient), GABOR, G-PHOG (Combined GABOR and PHOG) and profile feature by Marti-Bunke when applied in middle zone recognition of Bangla words using Hidden Markov Model (HMM) based framework. For this purpose, a zone segmentation method is applied to extract the busy (middle) zones of handwritten words and features are extracted from the middle zone. The system has been tested on a sufficiently large and variation-rich dataset consisting of 11,253 training and 3,856 testing data. From the experiment, it has been noted that PHOG feature outperforms other features in middle zone recognition. Since PHOG feature outperform others, we use this feature for full word recognition, For this purpose initially upper and lower zone components are recognized by PHOG features and SVM classifier. Finally, the zone-wise results are combined by the context information of the corresponding components in each zone to obtain the word level recognition. © 2015 IEEE.-
dc.description.sponsorshipABBYY;Google;iTESOFT;MyScript;Yooz-
dc.language.isoen_US-
dc.publisherIEEE Computer Society-
dc.relation.ispartofProceedings of the International Conference on Document Analysis and Recognition, ICDAR-
dc.subjectBangla Script Recognition-
dc.subjectHandwritten Text Recognition-
dc.subjectHidden Markov Model-
dc.titleA comparative study of features for handwritten Bangla text recognition-
dc.typeConference Paper-
dc.scopusid57188719920-
dc.scopusid57207436443-
dc.scopusid56880478500-
dc.scopusid57200742116-
dc.affiliationBhunia, A.K., Dept. of ECE, Inst. of Engg. and Management, Kolkata, India-
dc.affiliationDas, A., Dept. of ECE, Inst. of Engg. and Management, Kolkata, India-
dc.affiliationRoy, P.P., Dept. of CSE, Indian Institute of Technology, Roorkee, India-
dc.affiliationPal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India-
dc.identifier.conferencedetails13th International Conference on Document Analysis and Recognition, ICDAR 2015, 23-26 August 2015-
Appears in Collections:Conference Publications [CS]

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