no code implementations • 24 May 2024 • Abdur Rahman, Rajat Chawla, Muskaan Kumar, Arkajit Datta, Adarsh Jha, Mukunda NS, Ishaan Bhola
In the rapidly evolving landscape of AI research and application, Multimodal Large Language Models (MLLMs) have emerged as a transformative force, adept at interpreting and integrating information from diverse modalities such as text, images, and Graphical User Interfaces (GUIs).
1 code implementation • 27 Jun 2023 • Abdur Rahman, Arjun Ghosh, Chetan Arora
To address the limitations of previous works, which struggle to generalize to the intricacies of the Urdu script and the lack of sufficient annotated real-world data, we have introduced the UTRSet-Real, a large-scale annotated real-world dataset comprising over 11, 000 lines and UTRSet-Synth, a synthetic dataset with 20, 000 lines closely resembling real-world and made corrections to the ground truth of the existing IIITH dataset, making it a more reliable resource for future research.
Ranked #1 on Printed Text Recognition on UPTI
no code implementations • 8 Jan 2022 • Mirajul Islam, Jannatul Ferdous Ani, Abdur Rahman, Zakia Zaman
In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish.
no code implementations • 3 Jan 2022 • Yibin Wang, Abdur Rahman, W. Neil. Duggar, P. Russell Roberts, Toms V. Thomas, Linkan Bian, Haifeng Wang
However, manual annotation of lymph node region is a required data preprocessing step in most of the current ML-based ECE diagnosis studies.