Paper Info
Title
Text Proposals Based on Windowed Maximally Stable Extremal Region for Scene Text Detection
Abstract
The generation of text proposals (i.e. local candidate regions most likely containing textual components) is one critical and prerequisite step in scene text detection task. As one popular text proposal algorithm, the Maximally Stable Extremal Region (MSER), has been exploited by many successful text detection methods, while on the other hand has difficulties in handling complicated scene text involving touching characters and characters composed of multiple unconnected parts (e.g. Chinese characters and text in dot matrix fonts). In this paper, we propose a novel text proposal method for localizing text in natural images, which integrates the MSER algorithm with the multi-scale sliding window framework and efficiently extracts Windowed Maximally Stable Extremal Regions (WMSERs) as text proposals. We further present effective proposal filtering and grouping algorithms for exploiting WMSER-based proposals in text detection task. Experiments on public scene text datasets demonstrate the promising aspects of the proposed method in dealing with complicated scene text.
Year
DOI
Venue
2017
10.1109/ICDAR.2017.69
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Keywords
Field
DocType
text proposal,scene text detection,MSER,sliding window,random walk
Computer vision,Chinese characters,Histogram,Sliding window protocol,Task analysis,Pattern recognition,Computer science,Filter (signal processing),Feature extraction,Maximally stable extremal regions,Artificial intelligence,Dot matrix
Conference
Volume
ISSN
ISBN
01
1520-5363
978-1-5386-3587-2
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Feng Su117018.63
Wenjun Ding200.34
Lan Wang3121.54
Susu Shan470.78
Hailiang Xu582.22