Local Subspace Classifier with Transformation Invariance for Appearance-Based Character Recognition in Natural Images

Author

Higa, Keisuke ; Hotta, Seiji

Author_Institution

Div. of Adv. Inf. Technol. & Comput. Sci., Tokyo Univ. of Agric. & Technol., Koganei, Japan

fYear

2013

fDate

25-28 Aug. 2013

Firstpage

533

Lastpage

537

Abstract

This paper presents an appearance-based scheme for recognition of characters in natural images. In our method, we combine a local subspace classifier (LSC) and transformation-invariance with tangent vectors. In addition, we use negative images of original ones as new training samples for achieving high accuracy. Experimental results on Chars74K and ICDAR03-CH datasets show that the performance of our method is comparable to those of the feature-based state of the art.

Keywords

character recognition; image classification; learning (artificial intelligence); Chars74K dataset; ICDAR03-CH dataset; LSC; appearance-based character recognition; local subspace classifier; natural image recognition; tangent vectors; transformation invariance; Accuracy; Character recognition; Image recognition; Manifolds; Training; Transforms; Vectors; appearance-based; character recognition; transform-invariance;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition (ICDAR), 2013 12th International Conference on

Conference_Location

Washington, DC

ISSN

1520-5363

Type

conf

DOI

10.1109/ICDAR.2013.112

Filename

6628677

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3487216