P
Partha Pratim Roy
Researcher at Indian Institute of Technology Roorkee
Publications - 509
Citations - 8436
Partha Pratim Roy is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Chemistry & Medicine. The author has an hindex of 36, co-authored 404 publications receiving 5505 citations. Previous affiliations of Partha Pratim Roy include Samsung & Indian Statistical Institute.
Papers
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Journal ArticleDOI
Copper(I) and silver(I) complexes of anthraldehyde thiosemicarbazone: synthesis, structure elucidation, in vitro anti-tuberculosis/cytotoxic activity and interactions with DNA/HSA.
Ashiq Khan,Kamaldeep Paul,Iqubal Singh,Jerry P. Jasinski,Victoria A. Smolenski,Ethan P. Hotchkiss,Patrick Kelley,Zachary A. Shalit,Manpreet Kaur,Somesh Banerjee,Partha Pratim Roy,Rekha Sharma +11 more
TL;DR: Both the ligands (H1L and H2L) and their complexes (1-10) were tested for their anti-tubercular and anticancer activities and showed good interactions with DNA in docking studies.
Posted Content
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
Ayan Kumar Bhunia,Abir Bhowmick,Ankan Kumar Bhunia,Aishik Konwer,Prithaj Banerjee,Partha Pratim Roy,Umapada Pal +6 more
TL;DR: A novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition and has achieved superior performance compared to the other conventional approaches.
Proceedings ArticleDOI
An Efficient Coarse-to-Fine Indexing Technique for Fast Text Retrieval in Historical Documents
TL;DR: A fast text retrieval system to index and browse degraded historical documents, designed in a two level, coarse-to-fine approach, to increase the speed of the retrieval process.
Journal ArticleDOI
Recognizing gender from human facial regions using genetic algorithm
TL;DR: In this article, the frontal face image is divided into a number of distinct regions based on facial landmark points that are obtained by the Chehra model proposed by Asthana et al.
Proceedings ArticleDOI
Overwriting repetition and crossing-out detection in online handwritten text
TL;DR: This work proposes to use different density-based features to distinguish between "relevant" and "unwanted" (or noisy) parts of writing, and uses a 2-class HMM based classifier to get encouraging detection rate of unwanted regions from online handwritten text.