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Recognition of Handwritten Digits
Hekrdla, Michal ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This bachelors thesis inspects an issue of recognition of handwritten digits with decision trees method. It describes principle method, usage database NITS (National Institute of Standards and Technology) for purposes teaching algorithm, construction tags tree and decision tree. It describes too implementation those method on demonstrational program, which is its programme part. Finally it deal with testing recognition program and its estimation.
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Recognition of Handwritten Digits
Dobrovolný, Martin ; Mlích, Jozef (referee) ; Herout, Adam (advisor)
Recognition of handwritten digits is one of computer vision problematics that can not be solved with 100 % success these days. This document describes a method for handwritten digits recognizing based on shape features and randomized tree classifiers. These methods are known for their long time machine learning and quick characters recognizing. This method is due to use of relative angles among key locations and is nearly invariant to substantial affine and nonlinear deformations.
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Recognition of Handwritten Digits
Hekrdla, Michal ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This bachelors thesis inspects an issue of recognition of handwritten digits with decision trees method. It describes principle method, usage database NITS (National Institute of Standards and Technology) for purposes teaching algorithm, construction tags tree and decision tree. It describes too implementation those method on demonstrational program, which is its programme part. Finally it deal with testing recognition program and its estimation.
|
|
|
Recognition of Handwritten Digits
Dobrovolný, Martin ; Mlích, Jozef (referee) ; Herout, Adam (advisor)
Recognition of handwritten digits is one of computer vision problematics that can not be solved with 100 % success these days. This document describes a method for handwritten digits recognizing based on shape features and randomized tree classifiers. These methods are known for their long time machine learning and quick characters recognizing. This method is due to use of relative angles among key locations and is nearly invariant to substantial affine and nonlinear deformations.
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