The projects are part of the graduate-level course CSE-573 : Computer Vision and Image Processing [Spring 2019 @ UB_SUNY] Course Instructor : David Doerman (https://cse.buffalo.edu/~doermann/)
This project implements a series of exercises related to image processing, compression, filtering, segmentation, and optimization. The exercises explore key concepts in computer vision and image analysis, including quantization, Fourier transform, filtering, denoising, and object detection.
Code and supplementary results for the paper "S. M. Unter, E. L. Hertel, DDD - A Diagnostic Dataset for Character Recognition and Detection on Ancient Egyptian Hieratic Characters and Words" at the ICDAR 2026 conference.
Detect hidden Unicode characters & homoglyphs, reveals non-printable, zero-width and visually similar characters in text strings.
A simple OCR created using OpenCV's EAST model
EMNIST Detection built with TensorFlow
IIIT5K dataset converted to coco format along with python readable original label files. Original dataset is in matlab format, which might have been an issue for some potential users, hence this repository.
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