# Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network

arXiv: https://arxiv.org/abs/2101.08299 · HTML: https://mlsanity.com/paper/2101.08299/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents](https://mlsanity.com/paper/1705.03311/) (2017)

## Built on by (most cited first, 11 of 11)

- [Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods](https://mlsanity.com/paper/2203.12346/) (2022)
- [An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition using a Novel Transformers-based Model and an Innovative 270 Million-Words Multi-Font Corpus of Classical Arabic with Diacritics](https://mlsanity.com/paper/2208.11484/) (2022)
- [Online Gesture Recognition using Transformer and Natural Language Processing](https://mlsanity.com/paper/2305.03407/) (2023)
- [BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images](https://mlsanity.com/paper/2306.09351/) (2023)
- [Few-Shot Connectivity-Aware Text Line Segmentation in Historical Documents](https://mlsanity.com/paper/2508.19162/) (2025)
- [Combining Morphological and Histogram based Text Line Segmentation in the OCR Context](https://mlsanity.com/paper/2103.08922/) (2021)
- [Unsupervised learning of text line segmentation by differentiating coarse patterns](https://mlsanity.com/paper/2105.09405/) (2021)
- [LineCounter: Learning Handwritten Text Line Segmentation by Counting](https://mlsanity.com/paper/2105.11307/) (2021)
- [A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions](https://mlsanity.com/paper/2211.02643/) (2022)
- [CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents using Deep Feature Learning from JPEG Coefficients](https://mlsanity.com/paper/2308.06142/) (2023)
- [Application of deep learning approaches for medieval historical documents transcription](https://mlsanity.com/paper/2512.18865/) (2025)

## Abstract (arXiv)

This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inconsistent font types and sizes. In addition, they contain curved, multi-skewed and multi-directed side note lines within a complex page layout. Therefore, bounding polygon labeling would be very difficult and time consuming. Instead we rely on line masks that connect the components on the same text line. Then these line masks are predicted using a Fully Convolutional Network (FCN). In the literature, FCN has been successfully used for text line segmentation of regular handwritten document images. The present paper shows that FCN is useful with challenging manuscript images as well. Using a new evaluation metric that is sensitive to over segmentation as well as under segmentation, testing results on a publicly available challenging handwritten dataset are comparable with the results of a previous work on the same dataset.