Gabor feature extraction for character recognition: comparison with gradient feature

Liu, C.-L.; Koga, Masashi; Fujisawa, Hiromichi

doi:10.1109/icdar.2005.119

Cited by 64 publications

(29 citation statements)

References 10 publications

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“…A few comparative studies of Gabor and co-occurrence features for document segmentation and script and language identification are presented in [32,38]. More comparisons [23,6] can be found concerning Gabor features and gradient features for character recognition. Shahabi and Rahmati [40] propose a new method for writer identification of handwritten documents by combining Gabor and cooccurrence features.…”

Section: Introductionmentioning

confidence: 99%

Texture feature evaluation for segmentation of historical document images

Mehri

Gomez‐Krämer

Héroux

et al. 2013

Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing

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International audienceTexture feature analysis has undergone tremendous growth in recent years. It plays an important role for the analysis of many kinds of images. More recently, the use of texture analysis techniques for historical document image segmen-tation has become a logical and relevant choice in the conditions of significant document image degradation and in the context of lacking information on the document structure such as the document model and the typographical parameters. However, previous work in the use of texture analysis for segmentation of digitized historical document images has been limited to separately test one of the well-known texture-based approaches such as autocorrelation function, Grey Level Co-occurrence Matrix (GLCM), Gabor filters, gradient, wavelets, etc. In this paper we raise the question of which texture-based method could be better suited for discriminating on the one hand graphical regions from textual ones and on the other hand for separating textual regions with different sizes and fonts. The objective of this paper is to compare some of the well-known texture-based approaches: autocorrelation function, GLCM, and Gabor filters , used in a segmentation of digitized historical document images. Texture features are briefly described and quantitative results are obtained on simplified historical document images. The achieved results are very encouraging

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Section: Introductionmentioning

confidence: 99%

Texture feature evaluation for segmentation of historical document images

Mehri

Gomez‐Krämer

Héroux

et al. 2013

Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing

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show abstract

“…Feature-based approach can be implemented by computer vision technique in two phases: (1) extracts useful features using the presented algorithms; (2) uses extracted features to classify traffic signs (Daugman, 1985;Liu et al, 2005;Dalal and Triggs, 2005). Zhang et al (2010) use a binary tree of support vector machine (SVM) in local binary pattern (LBP) features for traffic sign recognition.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

Lin

Luo

et al. 2018

Period. Polytech. Transp. Eng.

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“…Features that extract relative angle and relative position from adjacent strokes have also been proposed [11]. Gabor feature [12] extracts local directions of strokes around each point of interest by scanning in several directions. None of those methods directly addresses our problem, that is, the importance of the global features of character strokes.…”

Section: Related Workmentioning

confidence: 99%

How Important is Global Structure for Characters?

Mori¹,

Uchida

Sakano

2012

2012 10th IAPR International Workshop on Document Analysis Systems

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Abstract-This paper studies the importance of the features that represent the global structure of character strokes to character recognition. Most existing character recognition methods based on character stroke features utilize a set or a sequence of local features such as xy-coordinates and local direction of strokes. This is natural from the viewpoint that each stroke is a trajectory and thus can be represented as a sequence of local features. This viewpoint, however, has a clear limitation in that local features cannot deal with global structure directly. For example, the sequence of local features cannot deal with the fact that the two end points of character "0" should be close to each other. In this paper we propose a simple and novel global feature that describes the global structure of the character shape of each class. We prove the importance of the global feature through a feature selection experiment. Specifically, we show that the global features are more often selected than local features to enhance classification accuracy under the AdaBoostbased machine learning framework. Recognition experiments using online numeral data show also that the use of global features improves recognition accuracy.

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Gabor feature extraction for character recognition: comparison with gradient feature

Cited by 64 publications

References 10 publications

Texture feature evaluation for segmentation of historical document images

Texture feature evaluation for segmentation of historical document images

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

How Important is Global Structure for Characters?

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