11 citations · 71 across the 35 of their papers we have counts for
7 papers · 1 filter
SketchLattice: Latticed Representation for Sketch Manipulation
Yonggang Qi, Guoyao Su, Pinaki Nath Chowdhury +2
The key challenge in designing a sketch representation lies with handling the abstract and iconic nature of sketches. Existing work predominantly utilizes either, (i) a pixelative…
Joint Visual Semantic Reasoning: Multi-Stage Decoder for Text Recognition
Ayan Kumar Bhunia, Aneeshan Sain, Amandeep Kumar +3
Although text recognition has significantly evolved over the years, state-of-the-art (SOTA) models still struggle in the wild scenarios due to complex backgrounds, varying fonts, u…
Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation
Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury +1
Text recognition remains a fundamental and extensively researched topic in computer vision, largely owing to its wide array of commercial applications. The challenging nature of th…
Towards the Unseen: Iterative Text Recognition by Distilling from Errors
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain +1
Visual text recognition is undoubtedly one of the most extensively researched topics in computer vision. Great progress have been made to date, with the latest models starting to f…
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition
Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar +3
Handwritten Text Recognition (HTR) remains a challenging problem to date, largely due to the varying writing styles that exist amongst us. Prior works however generally operate wit…
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain +3
A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack…
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Yongxin Yang +3
Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging…