Title :
A Feature Extraction Method for Cursive Character Recognition Using Higher-Order Singular Value Decomposition
Author :
Ameri, Mohammad Reza ; Haji, Mohsin ; Fischer, Anath ; Ponson, Dominique ; Bui, Tien D.
Author_Institution :
Comput. Sci. & Software Eng. Dept., Concordia Univ., Montreal, QC, Canada
Abstract :
The use of Higher-Order Singular Value Decomposition (HOSVD) and other tensor decomposition methods are popular in the face recognition domain, yet a direct application to handwritten character recognition has not shown promising results so far. Character recognition is commonly performed in two steps: feature extraction and classification. In this paper, we propose a feature extraction algorithm based on HOSVD which is then combined with standard statistical classification. The algorithm constructs a tensor from the training data and applies HOSVD in order to obtain a feature extractor matrix for arbitrary character images. We evaluate the proposed handwriting features in combination with SVM classification for character recognition on the CEDAR benchmark data set. The results indicate that our proposed approach significantly outperforms the standard HOSVD classification method.
Keywords :
face recognition; feature extraction; handwritten character recognition; singular value decomposition; support vector machines; tensors; CEDAR benchmark data set; HOSVD; SVM classification; cursive character recognition; face recognition; feature extraction; handwritten character recognition; higher-order singular value decomposition; tensor decomposition; Character recognition; Feature extraction; Handwriting recognition; Support vector machines; Tensile stress; Training; Feature evaluation and selection; higher-order singular value decomposition (HOSVD); optical character recognition; tensor decompositions;
Conference_Titel :
Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on
Conference_Location :
Heraklion
Print_ISBN :
978-1-4799-4335-7
DOI :
10.1109/ICFHR.2014.92