Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
- 25 June 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 29 (8), 1465-1469
- https://doi.org/10.1109/tpami.2007.1090
Abstract
The gradient direction histogram feature has shown superior performance in character recognition. To alleviate the effect of stroke direction distortion caused by shape normalization and provide higher recognition accuracies, we propose a new feature extraction approach, called normalization-cooperated gradient feature (NCGF) extraction, which maps the gradient direction elements of original image to direction planes without generating the normalized image and can be combined with various normalization methods. Experiments on handwritten Japanese and Chinese character databases show that, compared to normalization-based gradient feature, the NCGF reduces the recognition error rate by factors ranging from 8.63 percent to 14.97 percent with high confidence of significance when combined with pseudo-two-dimensional normalization.Keywords
This publication has 13 references indexed in Scilit:
- Gabor feature extraction for character recognition: comparison with gradient featurePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Handwritten digit recognition: investigation of normalization and feature extraction techniquesPattern Recognition, 2004
- Gradient feature extraction for classification-based face detectionPattern Recognition, 2003
- Handwritten digit recognition: benchmarking of state-of-the-art techniquesPattern Recognition, 2003
- Classification of handprinted Chinese characters using nonlinear normalization and correlation methodsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Two-dimensional extension of nonlinear normalization method using line density for character recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distanceIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
- Approximate Statistical Tests for Comparing Supervised Classification Learning AlgorithmsNeural Computation, 1998
- Improvement of handwritten Japanese character recognition using weighted direction code histogramPattern Recognition, 1997
- Modified Quadratic Discriminant Functions and the Application to Chinese Character RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1987