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Diwan, Bhoopendra Dhar
- Improve Efficiency of On-Line Handwriting Recognition Using Hidden Markov Models
Abstract Views :138 |
PDF Views:2
Authors
Affiliations
1 Dept. of Engineering, Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Dept. of Basic Sciences, Dr. C. V. Raman University, Bilaspur (C.G), IN
3 Dr. C. V. Raman University, Bilaspur (C.G), IN
1 Dept. of Engineering, Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Dept. of Basic Sciences, Dr. C. V. Raman University, Bilaspur (C.G), IN
3 Dr. C. V. Raman University, Bilaspur (C.G), IN
Source
Digital Image Processing, Vol 5, No 7 (2013), Pagination: 324-327Abstract
In this paper, as signatures continue to play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. to perform verification or identification of a signature, several steps must be performed. Online signature verification has been shown to achieve much higher verification rate than offline verification this paper proposes a novel framework for online signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input online signature and thus allow effective shape matching between registration and verification signatures. In addition, the online registrations enable robust recovery of the writing trajectory from an input online signature and thus allow effective shape matching between registration and verification signatures. in addition, the features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden markov models, support vector machines, and euclidean distance classifier. We propose several new techniques to improve the performance of the new signature verification rate system.Keywords
Verification Rate, Verification Rate, Hand Writing Recognition, Training Data, Testing Data.
- Wavelet Image Compression and Parameter Based Measurement
Abstract Views :171 |
PDF Views:2
In this research the different types of wavelet function are used. Wavelet transform is used to convert the pixel information into transform coefficient. Transform coefficient are quantized and then entropy coding is performed. For reconstruction entropy decoding and inverse wavelet transform are done. In this project a comparative study has been done using different wavelet function such as Haar, dB4 and dB6 for the compression and reconstruction of the image.
Authors
Affiliations
1 Department Of Engineering, Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Department of Basic Sciences, Dr. C. V. Raman University, Bilaspur (C.G), IN
3 Dr. C. V. Raman University, Bilaspur (C.G), IN
1 Department Of Engineering, Dr. C. V. Raman University, Bilaspur (C.G), IN
2 Department of Basic Sciences, Dr. C. V. Raman University, Bilaspur (C.G), IN
3 Dr. C. V. Raman University, Bilaspur (C.G), IN
Source
Digital Image Processing, Vol 5, No 5 (2013), Pagination: 264-267Abstract
The development of Digital image processing technology there are several applications. One of the applications is an image compression. The research assigned is to develop an Image Compression technique based on JPEG 2000 using Wavelet Transform For efficient representation of digital image in order to reduce the memory required for storage, improve the data access rate from storage device and reduce the bandwidth and time required for data transfer across communication channel wavelet transform is the best solution.In this research the different types of wavelet function are used. Wavelet transform is used to convert the pixel information into transform coefficient. Transform coefficient are quantized and then entropy coding is performed. For reconstruction entropy decoding and inverse wavelet transform are done. In this project a comparative study has been done using different wavelet function such as Haar, dB4 and dB6 for the compression and reconstruction of the image.