Title

Reproducible Research in Document Analysis and Recognition

Document Type

Conference Proceeding

Publication Date

4-13-2018

Publication Title

Advances in Intelligent Systems and Computing: Information Technology - New Generations

Publisher

Springer, Cham

Volume

738

First page number:

389

Last page number:

395

Abstract

With reproducible research becoming a de facto standard in computational sciences, many approaches have been explored to enable researchers in other disciplines to adopt this standard. In this paper, we explore the importance of reproducible research in the field of document analysis and recognition and in the Computer Science field as a whole. First, we report on the difficulties that one can face in trying to reproduce research in the current publication standards. These difficulties for a large percentage of research may include missing raw or original data, a lack of tidied up version of the data, no source code available, or lacking the software to run the experiment. Furthermore, even when we have all these tools available, we found it was not a trivial task to replicate the research due to lack of documentation and deprecated dependencies. In this paper, we offer a solution to these reproducible research.

Keywords

Reproducible research; Containers; Docker; OCRSpell; Document analysis and recognition

Disciplines

Operations Research, Systems Engineering and Industrial Engineering

Language

English

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