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 P. Nagabhushan
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, oscillating and touching text, and the presence of
skew become much more challenging when the case of complex handwritten document
images are considered for segmentation directly in their respective compressed
representation. This is because, the conventional way of processing compressed
documents is through decompression, but here in this paper, we propose an idea
that employs deep feature learning directly from the JPEG compressed
coefficients without full decompression to accomplish text-line localization in
the JPEG compressed domain. A modified U-Net architecture known as Compressed
Text-Line Localization Network (CompTLL-UNet) is designed to accomplish it. The
model is trained and tested with JPEG compressed version of benchmark datasets
including ICDAR2017 (cBAD) and ICDAR2019 (cBAD), reporting the state-of-the-art
performance with reduced storage and computational costs in the JPEG compressed
domain.