Evaluation of word spotting under improper segmentation scenario

Sounak Dey, Anguelos Nicolaou, Josep Lladós, Umapada Pal

Research output: Contribution to journalArticleResearch

Abstract

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.
Original languageEnglish
JournalInternational Journal on Document Analysis and Recognition
DOIs
Publication statusPublished - 1 Jan 2019

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