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Handwriting Recognition
Zouhar, David ; Řezníček, Ivo (referee) ; Mlích, Jozef (advisor)
This diploma thesis deals with handwriting recognition in real-time. It describes the ways how the intput data are processed. It is also focused on the classi cation methods, which are used for the recognition. It especially describes hidden Markov models. It also present the evaluation of the success of the recognition based on implemented experiments. The alternative keyboard for MeeGo system was created for this thesis as well. The established system achieved the success above 96%.
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Handwriting Recognition
Zouhar, David ; Řezníček, Ivo (referee) ; Mlích, Jozef (advisor)
This bachelor thesis deals with the handwritten character recognition in real time. It describes the ways how to obtain information for the text recognition, methods used in classification and it describes application made for getting text from drawn characters. It is also engaged in evaluation the created application. It deals with the experiments that were conducted to improve success of recognition. Thanks to the experiments, the success that was achieved was approximately 85%.
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Handwriting Recognition
Zouhar, David ; Řezníček, Ivo (referee) ; Mlích, Jozef (advisor)
This bachelor thesis deals with the handwritten character recognition in real time. It describes the ways how to obtain information for the text recognition, methods used in classification and it describes application made for getting text from drawn characters. It is also engaged in evaluation the created application. It deals with the experiments that were conducted to improve success of recognition. Thanks to the experiments, the success that was achieved was approximately 85%.
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Handwriting Recognition
Zouhar, David ; Řezníček, Ivo (referee) ; Mlích, Jozef (advisor)
This diploma thesis deals with handwriting recognition in real-time. It describes the ways how the intput data are processed. It is also focused on the classi cation methods, which are used for the recognition. It especially describes hidden Markov models. It also present the evaluation of the success of the recognition based on implemented experiments. The alternative keyboard for MeeGo system was created for this thesis as well. The established system achieved the success above 96%.
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