Publication: Evaluation of the Effect of Improper Segmentation on Word Spotting


Authors: Sounak Dey, Anguelos Nicolaou, Joseph Llados, and Umapada Pal


Published in: arxiv


Abstract:
Word spotting is an important recognition task in historical document analysis. In most cases methods are developed and evaluated assuming perfect word segmentations. In this paper we propose an experimental framework to quantify the effect of goodness of 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 apply the framework on the George Washington and Barcelona Marriage Dataset and on several established and state-of-the-art methods. The experiments allow for an estimate of the end-to-end performance of wordspotting methods.


pdf

bibtex: | @article{dey2016evaluation, title={Evaluation of the Effect of Improper Segmentation on Word Spotting}, author={Sounak Dey, Anguelos Nicolaou, Joseph Llados, and Umapada Pal}, journal={arXiv preprint }, year={2016} } —

Aditional Resources:

Dataset Distortion Generator: Given a Dataset in the format of GW, 100 different distorted versions can be created with the given python script. distortIoU.py

Experimental data and reproducabillity:

NA



Creative Commons License
Any work in this page other than source code or program binaries is licensed under a Creative Commons Attribution 4.0 International License. When applicable atribution should be in the form of a citation.