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Prasenjit Dey
Researcher at Microsoft
Publications - 6
Citations - 137
Prasenjit Dey is an academic researcher from Microsoft. The author has contributed to research in topics: Intelligent word recognition & Word recognition. The author has an hindex of 3, co-authored 6 publications receiving 114 citations. Previous affiliations of Prasenjit Dey include Future Institute of Engineering and Management & Indian Institute of Technology Kharagpur.
Papers
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Journal ArticleDOI
HMM-based Indic handwritten word recognition using zone segmentation
TL;DR: An efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones to reduce the number of distinct component classes compared to the total number of classes in Indic scripts is proposed.
Proceedings ArticleDOI
A Novel Approach of Bangla Handwritten Text Recognition Using HMM
TL;DR: A preliminary experiment is performed on a dataset of 10,120 Bangla handwritten words and it is found that the proposed approach outperforms the custom way of HMM based recognition.
Proceedings ArticleDOI
P-Simrank: Extending Simrank to Scale-Free Bipartite Networks
TL;DR: P-Simrank is introduced which extends the idea of Simrank to Scale-free bipartite networks and produces sub-optimal similarity scores in case of bipartITE graphs where degree distribution of vertices follow power-law.
Proceedings Article
A Framework for Mining Enterprise Risk and Risk Factors from News Documents
TL;DR: A risk analytics framework is presented that processes enterprise project management reports in the form of textual data and news documents and classify them into valid and invalid risk categories and extracts information from the text pertaining to the different categories of risks.
Proceedings ArticleDOI
Autonomous vision-guided approach for the analysis and grading of vertical suspension tests during Hammersmith Infant Neurological Examination (HINE)
TL;DR: A vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification, which results in automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE).