Paper Info
Title
Robust Seed Localization and Growing with Deep Convolutional Features for Scene Text Detection
Abstract
Text detection in natural scene images is an open and challenging problem due to the significant variations of the appearance of the text itself and its interaction with the context. In this paper, we present a novel text detection method based on robust localization and adaptive growing of seed text components. The method consists of two main ingredients. First, convolutional neural network is exploited to localize seed candidate characters from the maximally stable extremal regions of the image with learned discriminative deep convolutional features. Next, an iterative and adaptive growing algorithm is employed to grow from seed characters to search for other degraded text components in same text line based on their conformity to the seed, and an associative quality is learned to measure the conformity combining both the geometric and appearance constraints between two neighbouring text components. The effectiveness of the proposed method is demonstrated by the state-of-the-art results achieved on the public datasets.
Year
DOI
Venue
2015
10.1145/2671188.2749370
ICMR '15: International Conference on Multimedia Retrieval Shanghai China June, 2015
Keywords
Field
DocType
Text detection, convolutional neural network, MSER, natural scene image, SWT
Computer vision,Associative property,Pattern recognition,Computer science,Convolutional neural network,Maximally stable extremal regions,Artificial intelligence,Discriminative model,Machine learning,Text detection
Conference
ISBN
Citations 
PageRank 
978-1-4503-3274-3
3
0.40
References 
Authors
13
2
Name
Order
Citations
PageRank
Hailiang Xu182.22
Feng Su217018.63