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Optical Character Recognition at Camera Captured Images
Kindermann, Hubert ; Blažek, Jan (advisor) ; Kolomazník, Jan (referee)
We present solution of steps necessary for binarization and text lines detection contained in printed documents digitized by the camera. We introduce a normalization of non-uniform illumination method for text photographs. We propose input bitmap binarization algorithm based on two-dimensional probability pixel model which also considers its surrounding. We continue with description of robust text lines orientation detector based on optimization of risk function using first order derivatives of image function. In the end we present text lines detection and segmentation algorithm. Final shape of segmented lines is optimized with usage of graph algorithm. Powered by TCPDF (www.tcpdf.org)
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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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License Plate Detection and Recognition
Řepka, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This paper addresses the problem of object detection and recognition from still images using methods of computer vision. The objects of detection are czech license plates and the goal of this paper was to create an automatic license plate anotation tool. Suggested solution uses edge detection and machine learned cascading classifiers. Created application was then tested on dataset taken by the author.
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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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Forms of text data input and processing in business information systems - trends and current practices
Válková, Jana ; Stanovská, Iva (advisor) ; Hais, Petr (referee)
This thesis introduces readers to the basic types of the text and information inputs and processing to the computer. Thesis also includes historical contexts, current trends and future perspective of computer data input technologies and their use in practice. The first part of the thesis is a summary of a particular forms of entering and processing of the text data and information. The following part presents technological trends on the market concentrated on the automatic speech recognition systems along with the possibilities of their application in the business sphere. The rest of the thesis consists of a survey between Czech IT companies and based on it's results comes a suggestion of which technologies should be used as a part of the information systems.
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The Function of OCR Technology and its Usage in Document Processing
Pouzar, Aleš ; Horný, Stanislav (advisor) ; Krsek, Libor (referee)
Owing to a boom of information technologies optical character recognition has recently become a popular and affordable technology, enabling an effective way of sharing a large amount of information stored in paper documents. This work deals with systems for document conversion of analog data into electronic and editable form. It outlines the main methodology of OCR technology, evaluates a performance of OCR applications and analyses the contribution of imaging technologies through the benchmark tests on the set of paper documents.
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