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Found 3 papers, 1 papers with code
UTRNet: High-Resolution Urdu Text Recognition In Printed Documents
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
Fake Hilsa Fish Detection Using Machine Vision
In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish.
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
However, manual annotation of lymph node region is a required data preprocessing step in most of the current ML-based ECE diagnosis studies.
