A Survey of Historical Document Image Datasets

TL;DR

This paper systematically reviews 65 datasets for historical document image analysis, categorizing tasks, summarizing dataset features, and discussing challenges and standards to improve research comparability.

Contribution

It provides a comprehensive meta-study of existing datasets, including statistics, task types, and benchmarks, highlighting gaps and proposing standardization practices.

Findings

65 datasets analyzed with detailed summaries

Identification of gaps in dataset formats and evaluation metrics

Recommendations for standardization and conversion tools

Abstract

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.