3 citations · 5 across the 8 of their papers we have counts for
8 papers
CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents using Deep Feature Learning from JPEG Coefficients
Bulla Rajesh, Sk Mahafuz Zaman, Mohammed Javed +1
Automatic localization of text-lines in handwritten documents is still an open and challenging research problem. Various writing issues such as uneven spacing between the lines, os…
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network
Bulla Rajesh, Nandakishore Dusa, Mohammed Javed +2
The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from…
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks
Bulla Rajesh, Manav Kamlesh Agrawal, Milan Bhuva +2
Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Anlaysis (DIA) applications like word spotti…
HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using CNN-BiLSTM Network
Bulla Rajesh, Abhishek Kumar Gupta, Ayush Raj +2
Handwritten word recognition from document images using deep learning is an active research area in the field of Document Image Analysis and Recognition. In the present era of Big…
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique
Atul Sharma, Bulla Rajesh, Mohammed Javed
Plant leaf diseases pose a significant danger to food security and they cause depletion in quality and volume of production. Therefore accurate and timely detection of leaf disease…
Deep Learning Based Image Retrieval in the JPEG Compressed Domain
Shrikant Temburwar, Bulla Rajesh, Mohammed Javed
Content-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image repr…