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Handwriting and Signature Verification
Beránek, Jan ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This paper concerns methods of verification of person's signature and handwriting. Some of commonly used techniques are resumed and described with related literature being referred. Next aim of this work is design and implementation of a simple handwriting verification application. Application is based on edge detection and comparison of a set of structural and statistical features. As a support classification tool a SVM classifier of the LIBSVM software is employed. The Application is written in C language using OpenCV graphics library. Testing and training set was extracted from samples found in the IAM Handwriting Database. Application was created and tested in the Windows XP operating system.
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Edge Detection and Representation Algorithms for Object Recognition
Beránek, Jan ; Herout, Adam (referee) ; Španěl, Michal (advisor)
Main topic of this work are edge detection and representation algorithms used in an digital image. Some of typically used algorithms and methods are shown and described. Next aim of this work is design and description of implementation of a simple OCR system based on edge detection with use of an artificial neural net for a machine printed character recognition. The system was written in C programming language and uses OpenCV graphic library and FANN library for creating a neural net. Application was created and tested under Windows XP operating system.
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Handwritten Character Recognition Using Artificial Neural Networks
Horký, Vladimír ; Janda, Miloš (referee) ; Plchot, Oldřich (advisor)
Neural networks with algorithm back-propagation will be presented in this work. Theoretical background of the algorithm will be explained. The problems with training neural nets will be solving there. The work discuss some techniques of image preprocessing and image extraction features, which is one of main part in classification. Some part of work discuss few experiments with neural nets with chosen image features.
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Edge Detection and Representation Algorithms for Object Recognition
Beránek, Jan ; Herout, Adam (referee) ; Španěl, Michal (advisor)
Main topic of this work are edge detection and representation algorithms used in an digital image. Some of typically used algorithms and methods are shown and described. Next aim of this work is design and description of implementation of a simple OCR system based on edge detection with use of an artificial neural net for a machine printed character recognition. The system was written in C programming language and uses OpenCV graphic library and FANN library for creating a neural net. Application was created and tested under Windows XP operating system.
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Handwriting and Signature Verification
Beránek, Jan ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This paper concerns methods of verification of person's signature and handwriting. Some of commonly used techniques are resumed and described with related literature being referred. Next aim of this work is design and implementation of a simple handwriting verification application. Application is based on edge detection and comparison of a set of structural and statistical features. As a support classification tool a SVM classifier of the LIBSVM software is employed. The Application is written in C language using OpenCV graphics library. Testing and training set was extracted from samples found in the IAM Handwriting Database. Application was created and tested in the Windows XP operating system.
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Handwritten Character Recognition Using Artificial Neural Networks
Horký, Vladimír ; Janda, Miloš (referee) ; Plchot, Oldřich (advisor)
Neural networks with algorithm back-propagation will be presented in this work. Theoretical background of the algorithm will be explained. The problems with training neural nets will be solving there. The work discuss some techniques of image preprocessing and image extraction features, which is one of main part in classification. Some part of work discuss few experiments with neural nets with chosen image features.
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