Paper Info
Title
Bilateral Convolutional Activations Encoded With Fisher Vectors For Scene Character Recognition
Abstract
A rich and robust representation for scene characters plays a significant role in automatically understanding the text in images. In this letter, we focus on the issue of feature representation, and propose a novel encoding method named bilateral convolutional activations encoded with Fisher vectors (BCA-FV) for scene character recognition. Concretely, we first extract convolutional activation descriptors from convolutional maps and then build a bilateral convolutional activation map (BCAM) to capture the relationship between the convolutional activation response and the spatial structure information. Finally, in order to obtain the global feature representation, the BCAM is injected into FV to encode convolutional activation descriptors. Hence, the BCA-FV can effectively integrate the prominent features and spatial structure information for character representation. We verify our method on two widely used databases (ICDAR2003 and Chars74K), and the experimental results demonstrate that our method achieves better results than the state-of-the-art methods. In addition, we further validate the proposed BCA-FV on the "Pan+ChiPhoto" database for Chinese scene character recognition, and the experimental results show the good generalization ability of the proposed BCA-FV.
Year
DOI
Venue
2018
10.1587/transinf.2017EDL8238
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
bilateral convolutional activations, Fisher vectors, scene character recognition
Fisher vector,Pattern recognition,Character recognition,Computer science,Artificial intelligence
Journal
Volume
Issue
ISSN
E101D
5
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zhong Zhang114132.42
Hong Wang222.41
Shuang Liu33622.95
Tariq S. Durrani411733.56