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Datasets and Annotations for Document Analysis and Recognition

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Handbook of Document Image Processing and Recognition

Abstract

The definition of standard frameworks for performance evaluation is a key issue in order to advance the state-of-the-art in any field of document analysis since it permits a fair and objective comparison of different proposed methods under a common scenario. For that reason, a large number of public datasets have emerged in the last years. However, several challenges must be considered when creating such datasets in order to get a sufficiently large collection of representative data that can be easily exploited by the researchers. In this chapter we review different approaches followed by the document analysis community to address some of these challenges, such as the collection of representative data, its annotation with ground-truth information, or the representation using accepted and common formats. We also provide a comprehensive list of existing public datasets for each of the different areas of document analysis.

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Authors and Affiliations

  1. Departament de Ciències de la Computació, Computer Vision Center, Universitat Autònoma de Barcelona, Campus UAB – Edifici O, 08193, Bellaterra, Spain

    Ernest Valveny

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  1. Ernest Valveny
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Correspondence to Ernest Valveny .

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  1. University of Maryland, College Park, MD, USA

    David Doermann

  2. Université de Lorraine, Nancy, France

    Karl Tombre

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Valveny, E. (2014). Datasets and Annotations for Document Analysis and Recognition. In: Doermann, D., Tombre, K. (eds) Handbook of Document Image Processing and Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-859-1_32

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