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Mobile System for Text Recognition on Android
Tomešek, Jan ; Kolář, Martin (referee) ; Zemčík, Pavel (advisor)
This thesis deals with creation of a mobile library for preprocessing of images with text which represents a part of a system for text recognition. The library is realized with emphasis on generality of use, efficiency and portability. The library providing a set of algorithms primarily for image quality assessment and text detection was created in this thesis. These algorithms enable a substantial decrease in volume of transmitted data and speed up and refinement of the recognition process. An example application for the Android platform able to analyze composition of foods stated on their wrappings was created as well. Overall, the library (system) simplifies development of mobile applications with focus on text extraction and analysis. The mobile application then provides a comfortable way of food harmfulness verification. The thesis offers a reader an overview of current solutions and tools available in this field, it provides a breakdown of important image preprocessing algorithms and guides him through the construction of the library and the application for mobile devices.
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Recognition of Handwriting for Mobile Phones
Talaš, Vladimír ; Chalupníček, Kamil (referee) ; Schwarz, Petr (advisor)
The goal of this project is to create a mobile phone application, which can use phone camera to get a photography. This photography contains text, application has an ability to find a text, recognize all characters and send output as SMS. In this application there are implemented algorithms for text recognize from pictures based on Hidden Markovov Models. The particular stress is put on training of the model, to maximalise a succes of text recognition. There are some experiments model training with model variables, which are leading in better ability of text recognition. It was achieved a value of 97% succesfully recognized characters.
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Hand Writing Letters Recognition
Jelínek, Radek ; Žák, Jakub (referee) ; Zbořil, František (advisor)
The thesis deals with handwriting recognition and conversion into digital form. Recognition is focused on recognition of letters and finding success when you did not use the dictionary for word recognition. One part of document is compare with commercial applications.
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Social Network Analysis using methods of pattern recognition
Križan, Viliam ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
Diplomová práca sa zaoberá rozpoznávaním emócií z textu v sociálnych sieťach. Práca popisuje súčasné metódy extrakcie príznakov, používané lexikóny, korpusy a klasifikátory. Emócie boli rozpoznávané na základe klasifikátoru, netrénovaného na anotovaných dátach z mikroblogovacej siete Twitter. Výhodou použitia služby Twitter, bolo geografické vymedzenie dát, ktoré umožňuje sledovanie zmien emócií populácie v rôznych mestách. Prvým prístupom klasifikácie bolo vytvorenie Baseline algoritmu, ktorý používal jednoduchý lexikón. Pre zlepšenie klasifikácie sme v druhom bode použili komplexnejší SVM klasifikátor. SVM klasifikátory, extrakcie a selekcie príznakov boli použité z dostupnej Python knižnice Scikit. Dáta pre natrénovanie klasifikátoru boli zhromažďované z oblasti USA, a to s pomocou vytvorenej aplikácie. Klasifikátor bol natrénovaný na dátach, označených pri ich zhromažďovaní - bez manuálnej anotácie. Boli použité dve rôzne implantácie SVM klasifikátorov. Výsledné klasifikované emócie, v rôznych mestách a dňoch, boli zobrazené v podobe farebných značiek na mape.
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Text Recognition Enhanced by Writer Identity
Trněný, Matěj ; Kišš, Martin (referee) ; Kohút, Jan (advisor)
The objective of this theses was to implement a neural network for text recognition enhanced by writers identity. Adversarial learning method was selected for this purpose. Usefulness of this method was verified by experiments. This net should yield better results on data which are not similar to data contained in training data set. Accuracy of the resulting net was compared to method single-task learning and method multi-task learning. Net implementing single-task learning method has reached average character recognition error of 7, 995%, net implementing multi-task learning method has reached average error of 7, 565% and net implementing adversarial learning method has reached average error of 7, 573%. In comparison to the net implementing single-task learning multi-task learning has improvement of 5, 38% and adversarial learning has reached improvement of 5, 28%.
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Adaptation of Neural Networks to Target Writer
Sekula, Jakub ; Hradiš, Michal (referee) ; Kohút, Jan (advisor)
This bachelor's thesis deals with the adaptation of neural networks to a specific writer with an aim to improve recognition of handwritten text of this specific writer. The method that I use is fast, requires small training dataset and uses regularization, which tries to keep the distribution of regularized weights in adaptation network similar to the one in the original network. I tested this method on dataset of printed text called IMPACT and dataset of handwritten text. When testing on dataset of handwritten text I was able to improve recognition on two diaries with pre adaptation recognition error rate of 10,82 % and 1,82 % to 8,48 % and 0,77 % with a small number of adaptation iterations and using small amount of training lines. When testing on IMPACT dataset I was able to improve recognition error rate from 32,88 % to 5,30 %.
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Sudoku Solver for Android
Hrbas, Vojtěch ; Herout, Adam (referee) ; Páldy, Alexander (advisor)
This work deals with solving Sudoku game which is taken by a camera of a mobile device running Android. It discusses possibilities of image processing, possibilities of recognizing the text in the image and principle and solving of Sudoku game. It also examines existing applications for Android that solve Sudoku. Then it proposes the application itself for solving Sudoku and summarizes the results of testing the application in terms of performance and users.
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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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Optical Character Recognition
Suchý, Václav ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This paper describes problems of text recognition in picture. Discuss successes, advatages and disadvantages several methods of recognition. In second part there is described design and implementation of a simple OCR software for typewritten text recognition by using artificial neural networks.
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