S
Suman K. Ghosh
Researcher at Autonomous University of Barcelona
Publications - 17
Citations - 1609
Suman K. Ghosh is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Word (computer architecture) & Spotting. The author has an hindex of 9, co-authored 17 publications receiving 1116 citations. Previous affiliations of Suman K. Ghosh include University of Barcelona.
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Proceedings ArticleDOI
ICDAR 2015 competition on Robust Reading
Dimosthenis Karatzas,Lluis Gomez-Bigorda,Anguelos Nicolaou,Suman K. Ghosh,Andrew D. Bagdanov,Masakazu Iwamura,Jiri Matas,Lukas Neumann,Vijay Chandrasekhar,Shijian Lu,Faisal Shafait,Seiichi Uchida,Ernest Valveny +12 more
TL;DR: A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text and tasks assessing End-to-End system performance have been introduced to all Challenges.
Posted Content
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification
TL;DR: This paper models an offline writer independent signature verification task with a convolutional Siamese network, named SigNet, and exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
Proceedings ArticleDOI
A Two Stage Recognition Scheme for Handwritten Tamil Characters
TL;DR: An off-line recognition approach based on a database of handwritten Tamil characters provided acceptable classification accuracies on both the training and test sets of the present database.
Posted Content
Visual attention models for scene text recognition
TL;DR: In this article, a soft visual attention model was proposed to learn how to selectively focus on different parts of the image for text recognition, which achieved state-of-the-art performance in unconstrained text recognition.
Proceedings ArticleDOI
Visual Attention Models for Scene Text Recognition
TL;DR: In this article, a soft visual attention model was proposed to learn how to selectively focus on different parts of the image for text recognition, which achieved state-of-the-art performance in unconstrained text recognition.