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Methods used for OCR
Čermák, Marek ; Marada, Tomáš (referee) ; Zuth, Daniel (advisor)
Although OCR (Optical Character Recognition) is a topic which has been a subject of research since the second half of the 19th century, it has recieved a significant attention in the field of computer vision and object detection recently. This thesis presents history of OCR and briefly describes techniques which have been used over the course of time for character recognition. Main focus lies in the current text recognition methods introduced by soft computing. Since the major portion of the field is covered by neural networks, various architectures will be presented. Eventually a software for alphanumeric characters recognition will be implemented using a convolutional neural network.
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Recongition of symbols in digitalized mathematical expressions
Haas, František ; Valla, Tomáš (advisor) ; Mareš, Martin (referee)
This bachelor thesis focuses on finding suitable methods and algorithms for text segmentation and character recognition using artificial neural networks. Firstly, the thesis covers basic principles of artificial neuron and artificial neural networks, structure of convolutional neural networks and mainly backpropagation algorithm and stochastic Levenberg-Marquardt algorithm. Then the thesis describes image processing and image segmentation to single symbols using graph algorithms. This thesis also includes implementation of these methods and algorithms in an application which converts digital mathematical expressions to vector format.
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Car Licence Plate Detection and Recognition
Kovaříček, Roman ; Procházka, Boris (referee) ; Váňa, Jan (advisor)
This bachelor thesis deals with finding the license plates in the image and pattern recognition. Work describes short history of the state license plates. It deals with also the current state license plates and their problems. It analyzes the process of image segmentation and follow evaluation of selected areas. A part of the work is design and implementation of algorithms that solve candidate search areas or characters. The final step is the recognition of individual characters and display the user with details of the result.
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ANSI art
Novák, Vlastimil ; Slaný, Karel (referee) ; Vašíček, Zdeněk (advisor)
The present bachelor thesis deals with automatic generation of ASCII graphics. The thesis is divided in several parts. First part describes the basics of computer graphics, image processing and recognition. The next part is focused on a theory and history of two most famous art styles called ASCII art and ANSI art. The methods of char recognition and problems arising from their testing are described in software application design. The methods reducing colour space of analyzed area were designed for char foreground and background colouring. The semifinal part is focused on implementation of library generating ANSI and ASCII art image and software application enabling the setting of all necessary parameters. The testing on a series of images, evaluation of results reached during the development and suggestions of possible future extensions are described in last part of the thesis.
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Caligraphy Editor with Japanese Character Recognition
Horáček, Petr ; Žák, Pavel (referee) ; Švub, Miroslav (advisor)
This work focuses on creating an application to support Japanese character learning. It also contains a brief overview of Japanese writing's history and evolution. Based on the study of existing options, this work sets the requirements for the application. It discusses problems and tries to find possible solutions. Character recognition is an important part. The work describes chosen solutions and their implementations. It ends by demonstrating achieved results and discussing options for further development of the system.
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Application of neural net in image processing
Nagyová, Lenka ; Svobodová,, Jitka (referee) ; Boleček, Libor (advisor)
This work focuses on the theory of artificial neural networks: the history, individual ways of learning and architecture of networks. It is also necessary to desribe the image processing blocks from scanning and image processing through segmentation to object recognition. The next part is focused on connecting the previous two parts, and therefore on the use of neural networks in image processing, specifically the identification of objects. In the practical part of the work is designed the user application for recognizing characters such as numbers, uppercase and lowercase letters.
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Character recognition of real scenes using neural networks
Fiala, Petr ; Neumann, Lukáš (advisor) ; Berka, Petr (referee)
This thesis focuses on a problem of character recognition from real scenes, which has earned significant amount of attention with the development of modern technology. The aim of the paper is to use an algorithm that has state-of-art performance on standard data sets and apply it for the recognition task. The chosen algorithm is a convolution network with deep structure where the application of the specified model has not yet been published. The implemented solution is built on theoretical parts which are provided in comprehensive overview. Two types of neural network are used in the practical part: a multilayer perceptron and the convolution model. But as the complex structure of the convolution networks gives much better performance compare with the classification error of the MLP on the first data set, only the convolution structure is used in the further experiments. The model is validated on two public data sets that correspond with the specification of the task. In order to obtain an optimal solution based on the data structure several tests had been made on the modificated network and with various adjustments on the input data. Presented solution provided comparable prediction rate compare to the best results of the other studies while using artificially generated learning pattern. In conclusion, the thesis describes possible extensions and improvements of the model, which should lead to the decrease of the classification error.
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