@inproceedings{7c87519bc6f64a899d171a6cce088bf8,
title = "Scene text detection via integrated discrimination of component appearance and consensus",
abstract = "In this paper, we propose an approach to scene text detection that leverages both the appearance and consensus of connected components. A component appearance is modeled with an SVM based dictionary classifier and the component consensus is represented with color and spatial layout features. Responses of the dictionary classifier are integrated with the consensus features into a discriminative model, where the importance of features is determined with a text level training procedure. In text detection, hypotheses are generated on component pairs and an iterative extension procedure is used to aggregate hypotheses into text objects. In the detection procedure, the discriminative model is used to perform classification as well as control the extension. Experiments show that the proposed approach reaches the state of the art in both detection accuracy and computational efficiency, and in particularly, it performs best when dealing with low-resolution text in clutter backgrounds.",
keywords = "Component, Discrimination, Text detection",
author = "Qixiang Ye and David Doermann",
year = "2014",
doi = "10.1007/978-3-319-05167-3\_4",
language = "English",
isbn = "9783319051666",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "47--59",
booktitle = "Camera-Based Document Analysis and Recognition - 5th International Workshop, CBDAR 2013, Revised Selected Papers",
address = "Germany",
note = "5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013 ; Conference date: 23-08-2013 Through 23-08-2013",
}