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A Real-World Noisy Unstructured Handwritten Notebook Corpus for Document Image Analysis Research

Jin Chen 1 Daniel Lopresti 1 Bart Lamiroy 2 
2 QGAR - Querying Graphics through Analysis and Recognition
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Traditionally, document image analysis (DIA) is conducted on datasets that are prepared for research purposes. Many existing handwriting datasets, however, do not necessarily represent the range of problems we wish to solve in real life. In this work, we introduce a noisy and unstructured handwriting dataset that aims for promoting and evaluating robust document analysis algorithms for real-world challenges, as a result of emphasizing the process of building and curating a dataset. First, we explain the data acquisition process and characterize its critical features as noisy and unstructured. Then, we discuss a set of real-world scenarios that might benefit from using our notebook dataset. As an on-going activity, so far we have collected 18 handwritten note-books from nine college students, resulting in a total of 499 pages. We expect to collect over 100 notebooks, or equivalently about 3,000 pages, from at least 50 students. This dataset is available to the research community via the Lehigh document analysis and exploitation (DAE) platform.
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Submitted on : Thursday, September 29, 2011 - 4:36:58 PM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM




Jin Chen, Daniel Lopresti, Bart Lamiroy. A Real-World Noisy Unstructured Handwritten Notebook Corpus for Document Image Analysis Research. Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data - (J-MOCR-AND 2011), IAPR, Sep 2011, Beijing, China. ⟨10.1145/2034617.2034620⟩. ⟨inria-00627844⟩



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