# CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents using Deep Feature Learning from JPEG Coefficients

arXiv: https://arxiv.org/abs/2308.06142 · HTML: https://mlsanity.com/paper/2308.06142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Residual Learning in the JPEG Transform Domain](https://mlsanity.com/paper/1812.11690/) (2018)
- [DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients](https://mlsanity.com/paper/1907.11503/) (2019)
- [Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network](https://mlsanity.com/paper/2101.08299/) (2021)
- [Semantic Segmentation in Learned Compressed Domain](https://mlsanity.com/paper/2209.01355/) (2022)
- [Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm](https://mlsanity.com/paper/1901.11477/) (2019)
- [DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents](https://mlsanity.com/paper/2306.01359/) (2023)

## Abstract (arXiv)

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.