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User Interface for Efficient Corrections of OCR Output
Szepsi, Pavol ; Kapinus, Michal (referee) ; Hradiš, Michal (advisor)
The aim of the present bachelor thesis was to design and implement a web user interface for checking and correcting OCR outputs which will be suitable for mobile and touchscreen devices. The user interface uses the OCR output variants that the user can use to modify the recognized text. The interface is implemented in JavaScript using the Vue JS framework. XAMPP package is used for the server part. The tool Axios is used for communication between the user interface and the server. The created interface allows users to quickly and easily correct the OCR outputs, either on a computer or on a mobile device.
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Neural Networks for Automatic Table Recognition
Piwowarski, Lukáš ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Tato práce seznamuje čtenáře se současnými technikami rozpoznávání tabulek, které se používají především k získávání informací z ručně psaných nebo tištěných historických tabulek. Představujeme také metodu založenou na grafové neuronové síti, která je inspirována představenými přístupy. Metoda se skládá ze tří fází: fáze inicializace grafu, fáze klasifikace uzlů/hran a fáze transformace grafu na text. Ve fázi inicializace grafu používáme algoritmus viditelnosti uzlů a OCR k vytvoření počáteční grafové reprezentace vstupní tabulky. Ve fázi klasifikace uzlů a hran jsou uzly a hrany klasifikovány a ve fázi transformace grafu na text zarovnáváme uzly grafu do mřížky, která je pak použita k vytvoření konečné textové reprezentace tabulky. Náš implementovaný model byl schopen dosáhnout přesnosti 68 % u detekce horizontálních sousedů, přesnosti 71 % u detekce vertikálních sousedů a přesnosti 83 % u detekce buněk na datové sadě ABP.
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Character recognition of machine-written documents
Kindermann, Hubert ; Blažek, Jan (advisor) ; Kolomazník, Jan (referee)
In the present thesis we solve the problem of symbol extraction and recognition from printed documents digitized by the scanner or camera. We introduce a noise resistant algorithm of document lighting normalization. We continue with the extraction of individual characters from the document and their recognition with a system of feedforward multilayer neural networks. We also focus on processing of the resulting set of recognized characters, which is necessary for further use of the extracted text. The last step is correction of the output based on surrounding letters of each character. We have successfully implemented an automatic system containing all the above components.
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Handwriting recognition using neural network
Petr, Martin ; Surynek, Pavel (advisor) ; Pergel, Martin (referee)
Title: Handwriting recognition using neural network Author: Martin Petr Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: RNDr. Pavel Surynek, PhD. Supervisor's e-mail address: pavel.surynek@mff.cuni.cz Abstract: Pattern recognition finds its use in many fields whose development has been affected by computer science and computer technology. Among these, the conversion of handwritten or printed text into computer-encoded text has a particularly prominent position. In the presented work we propose a method for recognizing handwritten characters in real-time using feedforward neural network as the basic classification mechanism. Dealing with differences among individual instances of each handwritten character we thoroughly explored the possibility of suppressing these while emphasizing characteristics that are essential for successful recognition. For these purposes we employed discrete cosine transform, whose time-proven application in audio and video signal processing or even directly in the field of pattern recognition provided a convincing argument for us to use it in our work as well. As a means of suppressing variations among various writing instruments we proposed preprocessing of input images using binarization and skeletonization. The designed method was...
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Automatic Acquisition Of Values From Measurement Devices Without Communication Interface
Dohnálek, Martin
This paper deals with optical character recognition (OCR) of measured values from displaysof measuring instruments without communication interface. Proposed algorithm functions asa bridging between a screen of the instrument and a measuring software. It enables the acquisitionof the displayed value from a snapped picture from the input camera stream, so that it is comprehensiblefor computers. The execution is, after necessary initialization done by the user, fully automated.Supported camera connection interfaces are USB and WiFi, meaning that either standardoffice webcam, or smartphone with third party app running is usable. The final algorithm recognizedboth 99 % valid values and its speed was exceeding the refresh rate of most common instruments(as fast as 34 ms per iteration). This means that it is not bottlenecking the measurement itself.
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Active Learning for Processing of Archive Sources
Hříbek, David ; Zbořil, František (referee) ; Rozman, Jaroslav (advisor)
This work deals with the creation of a system that allows uploading and annotating scans of historical documents and subsequent active learning of models for character recognition (OCR) on available annotations (marked lines and their transcripts). The work describes the process, classifies the techniques and presents an existing system for character recognition. Above all, emphasis is placed on machine learning methods. Furthermore, the methods of active learning are explained and a method of active learning of available OCR models from annotated scans is proposed. The rest of the work deals with a system design, implementation, available datasets, evaluation of self-created OCR model and testing of the entire system.
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Automatic acquisition of values from measurement devices without communication interface
Dohnálek, Martin ; Čala, Martin (referee) ; Kunz, Jan (advisor)
This bachelor thesis deals with the matter of optical character recognition from displays of measurement devices without communication interface. This would allow carrying out automated experiments using cheaper or older gear, which is not endowed with means for direct connection to a computer. Input image necessary for the character recognition is acquired using a camera pointed at a display of the device. The recognition is afterwards performed on periodically captured image based on an already existing dataset for particular apparatus. The output of the algorithm is a file containing recognized values, units, and timestamps of the recognition. The tool for creating datasets was designed as well. The achieved speed of recognition (as fast as 34 ms per iteration) during practical testing confirmed the sufficient optimalization of OCR algorithm. On the other hand, the determined hit rate of recognition abiding specified conditions was nearly 100 %. Lastly, the resistance to misalignment of display and sensor plane was monitored. The OCR algorithm is resilient to horizontal tilt up to +/- 5° and vertical tilt up to +/- 20°.
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Vehicle License Plate Detection and Recognition Software
Masaryk, Adam ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The aim of this bachelor thesis is to design and develop software that can detect and recognize license plates from images. The software is divided into 3 parts - license plates detection, detector output processing and license plates characters recognition. We decided to implement detection and recognition using modern methods using convolutional neural networks.
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License Plate Recognition
Mrhač, Ondřej ; Sochor, Jakub (referee) ; Navrátil, Jan (advisor)
This thesis talks about problematics of licence plate detection, licence plate recognitionand my implementation for device i.MX 6 Series of NXP semiconductors s.r.o company. Model program for licence plate detection and recognition is written with help of OpenCV library and engine Tesseract and it’s successfully put into operation on this device. Afterwards program was measured his runtime on PC and i.MX6 Series device and those measurements were compared. At the end of this thesis were found the most demanding parts of the program. Future changes and improvements were proposed.
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Mobile System for Text Recognition on iOS
Bobák, Petr ; Sochor, Jakub (referee) ; Zemčík, Pavel (advisor)
This thesis describes a development of a modern client-server application for text recognition on iOS platform. The reader is acquainted with common principles of a client-server model, including its known architecture styles, and with a distribution of logical layers between both sides of the model. After that the thesis depicts current trends and examples of suitable technologies for creating an application programming interface of a web server. Possible ways of text recognition on the server side are discussed as well. In context of a client side, the thesis provides an insight into iOS platform and a few important concepts in iOS application development. Following implementation of the server side is stressed to be reusable as much as possible for different kinds of use cases. Last but not least, the thesis provides a simple iOS framework for a direct communication with the recognition server. Finally, an application for evaluation of food ingredients from a packaging material is implemented as an example of usage.
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