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Simple Character Recognition
Hamrský, Jan ; Svoboda, Pavel (referee) ; Polok, Lukáš (advisor)
This work deals with the process of text location and recognition in an image document. It discusses the matter of feature extraction and its usage in machine learning. Portion of this work is devoted to design and implementation of application for simple character recognition of machine printed text.
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Defect detection using smart camera
Hons, Viktor ; Boštík, Ondřej (referee) ; Honec, Peter (advisor)
This thesis deals with the application of smart cameras and verification of its functions. In the first part the term smart camera is defined, the parts of it and the most common applications are presented. A review of smart cameras from the different manufactures on the market is made. After selection of the proper camera model three task from real industrial application are specified – inspection of capacitor print, inspection of beer label and dimension measurement. With the picked camera the tasks are performed, including the layout of workplace, scene and lighting. Further the reliability is tested together with the successfulness and the speed of designed solution.
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Mobile Interpreter for Android
Homola, Vladimír ; Maršík, Lukáš (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis describes implementation of the Mobile interpreter application with focus on its user interface. The goal is to create an application with such interface with which the users will be able to work effectively and with pleasure. The first part contains a summary of the knowledge learned during study of this problem. After that is definition of the future user, situations in which he used it and design of system and its interface. Description of implementation and user testing is in the last part.
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OCR Trained with Unanotated Data
Buchal, Petr ; Dobeš, Petr (referee) ; Hradiš, Michal (advisor)
The creation of a high-quality optical character recognition system (OCR) requires a large amount of labeled data. Obtaining, or in other words creating, such a quantity of labeled data is a costly process. This thesis focuses on several methods which efficiently use unlabeled data for the training of an OCR neural network. The proposed methods fall into the category of self-training algorithms. The general approach of all proposed methods can be summarized as follows. Firstly, the seed model is trained on a limited amount of labeled data. Then, the seed model in combination with the language model is used for producing pseudo-labels for unlabeled data. Machine-labeled data are then combined with the training data used for the creation of the seed model and they are used again for the creation of the target model. The successfulness of individual methods is measured on the handwritten ICFHR 2014 Bentham dataset. Experiments were conducted on two datasets which represented different degrees of labeled data availability. The best model trained on the smaller dataset achieved 3.70 CER [%], which is a relative improvement of 42 % in comparison with the seed model, and the best model trained on the bigger dataset achieved 1.90 CER [%], which is a relative improvement of 26 % in comparison with the seed model. This thesis shows that the proposed methods can be efficiently used to improve the OCR error rate by means of unlabeled data.
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Deep Learning for OCR in GUI
Hamerník, Pavel ; Špaňhel, Jakub (referee) ; Lysek, Tomáš (advisor)
Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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Support for Codenames Game on Mobile Phone with OS Android
Hurta, Martin ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an support application for word association board game Codenames on mobile phones with operating system Android. The solution consists of detection and recognition of the game board using the OpenCV and Tess-two libraries and Google Firebase ML Kit tools and providing support during the game, including an optional level of assistance and the ability to play on multiple devices with Google Play Games services. These features motivate the user to further use the application and provide data in~the form of generated game records, that are useful for further development and validation of association models or strategies for automatic playing.
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Deep Learning for OCR in GUI
Hamerník, Pavel ; Špaňhel, Jakub (referee) ; Lysek, Tomáš (advisor)
Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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OCR Trained with Unanotated Data
Buchal, Petr ; Dobeš, Petr (referee) ; Hradiš, Michal (advisor)
The creation of a high-quality optical character recognition system (OCR) requires a large amount of labeled data. Obtaining, or in other words creating, such a quantity of labeled data is a costly process. This thesis focuses on several methods which efficiently use unlabeled data for the training of an OCR neural network. The proposed methods fall into the category of self-training algorithms. The general approach of all proposed methods can be summarized as follows. Firstly, the seed model is trained on a limited amount of labeled data. Then, the seed model in combination with the language model is used for producing pseudo-labels for unlabeled data. Machine-labeled data are then combined with the training data used for the creation of the seed model and they are used again for the creation of the target model. The successfulness of individual methods is measured on the handwritten ICFHR 2014 Bentham dataset. Experiments were conducted on two datasets which represented different degrees of labeled data availability. The best model trained on the smaller dataset achieved 3.70 CER [%], which is a relative improvement of 42 % in comparison with the seed model, and the best model trained on the bigger dataset achieved 1.90 CER [%], which is a relative improvement of 26 % in comparison with the seed model. This thesis shows that the proposed methods can be efficiently used to improve the OCR error rate by means of unlabeled data.
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Defect detection using smart camera
Hons, Viktor ; Boštík, Ondřej (referee) ; Honec, Peter (advisor)
This thesis deals with the application of smart cameras and verification of its functions. In the first part the term smart camera is defined, the parts of it and the most common applications are presented. A review of smart cameras from the different manufactures on the market is made. After selection of the proper camera model three task from real industrial application are specified – inspection of capacitor print, inspection of beer label and dimension measurement. With the picked camera the tasks are performed, including the layout of workplace, scene and lighting. Further the reliability is tested together with the successfulness and the speed of designed solution.
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Deep Learning for OCR in GUI
Hamerník, Pavel ; Špaňhel, Jakub (referee) ; Lysek, Tomáš (advisor)
Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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