# READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

arXiv: https://arxiv.org/abs/1705.03311 · HTML: https://mlsanity.com/paper/1705.03311/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

## Built on by (most cited first, 18 of 18)

- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network](https://mlsanity.com/paper/2101.08299/) (2021)
- [Digital Peter: Dataset, Competition and Handwriting Recognition Methods](https://mlsanity.com/paper/2103.09354/) (2021)
- [Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods](https://mlsanity.com/paper/2203.12346/) (2022)
- [Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records](https://mlsanity.com/paper/2304.14044/) (2023)
- [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)
- [ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)](https://mlsanity.com/paper/2509.12965/) (2025)
- [Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data](https://mlsanity.com/paper/2107.06777/) (2021)
- [Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis](https://mlsanity.com/paper/2106.11797/) (2021)
- [Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers](https://mlsanity.com/paper/2109.08477/) (2021)
- [Entry Separation using a Mixed Visual and Textual Language Model: Application to 19th century French Trade Directories](https://mlsanity.com/paper/2302.08948/) (2023)
- [A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR](https://mlsanity.com/paper/2604.00725/) (2026)
- [Confidence Estimation for Object Detection in Document Images](https://mlsanity.com/paper/2208.13391/) (2022)
- [REE-HDSC: Recognizing Extracted Entities for the Historical Database Suriname Curacao](https://mlsanity.com/paper/2401.02972/) (2023)
- [End-to-End Text Line Detection and Ordering](https://mlsanity.com/paper/2606.04166/) (2026)
- [OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios](https://mlsanity.com/paper/2608.18586/) (2026)
- [Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts](https://mlsanity.com/paper/2608.18696/) (2026)
- [PalmLeaf-VQA: A Multi-Script Visual Question Answering Benchmark for Historical Palm-Leaf Manuscript Understanding Across Diverse Regions](https://mlsanity.com/paper/2609.31651/) (2026)

## Abstract (arXiv)

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.