# BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts

arXiv: https://arxiv.org/abs/1907.04041 · HTML: https://mlsanity.com/paper/1907.04041/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition](https://mlsanity.com/paper/2406.09630/) (2024)
- [U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts](https://mlsanity.com/paper/2401.08425/) (2024)
- [You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine](https://mlsanity.com/paper/2207.11230/) (2022)
- [Digital Editions as Distant Supervision for Layout Analysis of Printed Books](https://mlsanity.com/paper/2112.12703/) (2021)
- [New Results for the Text Recognition of Arabic Maghrib=i Manuscripts -- Managing an Under-resourced Script](https://mlsanity.com/paper/2211.16147/) (2022)
- [Advances and Limitations in Open Source Arabic-Script OCR: A Case Study](https://mlsanity.com/paper/2402.10943/) (2024)
- [Arabic Handwritten Text Line Dataset](https://mlsanity.com/paper/2312.07573/) (2023)
- [A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation](https://mlsanity.com/paper/2509.02864/) (2025)
- [When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study](https://mlsanity.com/paper/2608.22366/) (2026)
- [AraMS-28k: The Largest Publicly Released Line-Level Dataset of Historical Arabic Manuscripts with Margin and Insertion-Anchor Annotations](https://mlsanity.com/paper/2608.26921/) (2026)

## Abstract (arXiv)

The application of handwritten text recognition to historical works is highly dependant on accurate text line retrieval. A number of systems utilizing a robust baseline detection paradigm have emerged recently but the advancement of layout analysis methods for challenging scripts is held back by the lack of well-established datasets including works in non-Latin scripts. We present a dataset of 400 annotated document images from different domains and time periods. A short elaboration on the particular challenges posed by handwriting in Arabic script for layout analysis and subsequent processing steps is given. Lastly, we propose a method based on a fully convolutional encoder-decoder network to extract arbitrarily shaped text line images from manuscripts.