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Semiautomatic Collection of Large Database of Handwritten Letters
Štěpánek, Ivo ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This thesis deals with creation of database of handwritten characters further usable for handwritten character recognition. The issue of systems for unconstrained handwritten text recognition and datasets usable for it is discussed. Practical part of the thesis aims at preprocessing of the input document and line and word segmentation followed by extraction of isolated characters. These phases can be done entirely automatically, though user input to correct output of automatic processing is supposed. Furthermore the practical part is devoted to annotation of obtained character and to generation of XML document containing annotation and position of single characters from the input texxt. The created system is finally evaluated with emphasis on GUI and automatic segmentation succes rate.
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Detection of Logopaedic Defects in Speech
Pešek, Milan ; Smékal, Zdeněk (referee) ; Atassi, Hicham (advisor)
The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
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Semiautomatic Collection of Large Database of Handwritten Letters
Štěpánek, Ivo ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This thesis deals with creation of database of handwritten characters further usable for handwritten character recognition. The issue of systems for unconstrained handwritten text recognition and datasets usable for it is discussed. Practical part of the thesis aims at preprocessing of the input document and line and word segmentation followed by extraction of isolated characters. These phases can be done entirely automatically, though user input to correct output of automatic processing is supposed. Furthermore the practical part is devoted to annotation of obtained character and to generation of XML document containing annotation and position of single characters from the input texxt. The created system is finally evaluated with emphasis on GUI and automatic segmentation succes rate.
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Detection of Logopaedic Defects in Speech
Pešek, Milan ; Smékal, Zdeněk (referee) ; Atassi, Hicham (advisor)
The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
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