Wavelet Descriptors for Recognition of Basic Symbols in Printed Kannada Text

Kunte, R. Sanjeev; Samuel, R. D. Sudhaker

doi:10.1142/s0219691307001793

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…However, only a fewer utilize wavelets [ 16,21,22,24,29,30]. In this section, several examples [21,22,28,29] are briefly introduced.…”

Section: Handwritten and Printed Character Recognitionmentioning

confidence: 99%

“…N Analysis and detection of singularities with wavelets [10][11][12][13][14][15][16][17][18][19][20], N Wavelet descriptors for shapes of the objects [12,[21][22][23][24], N Invariant representation of patterns [25][26][27], N Handwritten and printed character recognition [16,21,22,24,[28][29][30], N Texture analysis and classification [25,[31][32][33][34], N Image indexing and retrieval [35][36][37][38][39][40], N Classification and clustering [41][42][43], N Document analysis with wavelets [17,[44][45][46][47], N Iris pattern recognition [48], N Face recognition using wavelet transform [49][50]…”

Section: Introductionmentioning

confidence: 99%

“…11 Diagram of ring-projection-wavelet-fractal methodFig. 12 Some examples of Kannada text in Indian languages[28] …”

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confidence: 99%

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Status of pattern recognition with wavelet analysis

Tang

2008

Front. Comput. Sci. China

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Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years. Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence, information security, biometrics, military science and technology, finance and economics, weather forecast, image processing, communication, biomedical engineering, document processing, robot vision, transportation, and endless other areas, with many encouraging results. The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis. This paper aims at a brief survey of pattern recognition with the wavelet theory. It contains the following respects: analysis and detection of singularities with wavelets; wavelet descriptors for shapes of the objects; invariant representation of patterns; handwritten and printed character recognition; texture analysis and classification; image indexing and retrieval; classification and clustering; document analysis with wavelets; iris pattern recognition; face recognition using wavelet transform; hand gestures classification; character processing with B-spline wavelet transform; wavelet-based image fusion, and others.

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“…However, only a fewer utilize wavelets [ 16,21,22,24,29,30]. In this section, several examples [21,22,28,29] are briefly introduced.…”

Section: Handwritten and Printed Character Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Status of pattern recognition with wavelet analysis

Tang

2008

Front. Comput. Sci. China

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“…To summarize, Kannada alphabet has (i) 16 vowels, (ii) 34 consonants, each with 34 basic form and 16 consonantvowel composite letters giving rise to 578 (34 x 17) characters and (iii) 34 subscripts to a total of 628 (16+578+34) characters. The full set of Kannada alphabet is given in [9].…”

Section: Kannada Scriptmentioning

confidence: 99%

“…The character contour is extracted from the segmented character by classifying the pixels of character into interior, noise and contour points [11], and is then normalized to make the character representation invariant to different transformations such as size, shift and numbers of contour points [9]. For a given character (Kannada or English) contour, its wavelet features are extracted by applying Discrete Wavelet Transform to the contour points to get wavelet descriptors which serve as features for the character which the pre-trained neural classifiers uses to recognize the character and generate the class information.…”

Section: System Architecturementioning

confidence: 99%

A Bilingual Machine-Interface OCR for Printed Kannada and English Text Employing Wavelet Features

Kunte¹,

Samuel²

2007

10th International Conference on Information Technology (ICIT 2007)

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An Optical Character Recognition (OCR) system is one of the important research areas in the field of Humanmachine interface. This paper presents a bilingual OCR system for printed Kannada and English text. Gabor filter based features are used for separating the Kannada and English words from the bilingual document. Wavelets that have been progressively used in pattern recognition are used in the system to extract the features for classifying both the Kannada and English characters. Multilayer feed forward Neural classifiers known for their good generalization and approximation property have been effectively used in the system for the classification. An overall recognition rate of 90.5% is obtained at character level. 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00 © 2007 IEEE DOI 196 10th International Conference on Information Technology 0-7695-3068-0/07 $25.00

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Thermal Human Face Recognition Based on Haar Wavelet Transform and Series Matching Technique

Seal

Ganguly

Bhattacharjee

et al. 2013

Lecture Notes in Electrical Engineering

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Thermal infrared (IR) images represent the heat patterns emitted from hot object and they don't consider the energies reflected from an object. Objects living or non-living emit different amounts of IR energy according to their body temperature and characteristics. Humans are homoeothermic and hence capable of maintaining constant temperature under different surrounding temperature. Face recognition from thermal (IR) images should focus on changes of temperature on facial blood vessels. These temperature changes can be regarded as texture features of images and wavelet transform is a very good tool to analyze multi-scale and multi-directional texture. Wavelet transform is also used for image dimensionality reduction, by removing redundancies and preserving original features of the image. The sizes of the facial images are normally large. So, the wavelet transform is used before image similarity is measured. Therefore this paper describes an efficient approach of human face recognition based on wavelet transform from thermal IR images. The system consists of three steps. At the very first step, human thermal IR face image is preprocessed and the face region is only cropped from the entire image. Secondly, "Haar" wavelet is used to extract low frequency band from the cropped face region. Lastly, the image classification between the training images and the test images is done, which is based on low-frequency components. The proposed approach is tested on a number of human thermal infrared face images created at our own laboratory and "Terravic Facial IR Database". Experimental results indicated that the thermal infra red face images can be recognized by the proposed system effectively. The maximum success of 95% recognition has been achieved.

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Wavelet Descriptors for Recognition of Basic Symbols in Printed Kannada Text

Cited by 16 publications

References 9 publications

Status of pattern recognition with wavelet analysis

Status of pattern recognition with wavelet analysis

A Bilingual Machine-Interface OCR for Printed Kannada and English Text Employing Wavelet Features

Thermal Human Face Recognition Based on Haar Wavelet Transform and Series Matching Technique

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