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Object Detection in Video Sequences
Šebela, Miroslav ; Beneš, Radek (referee) ; Číka, Petr (advisor)
The thesis consists of three parts. Theoretical description of digital image processing, optical character recognition and design of system for car licence plate recognition (LPR) in image or video sequence. Theoretical part describes image representation, smoothing, methods used for blob segmentation and proposed are two methods for optical character recognition (OCR). Concern of practical part is to find solution and design procedure for LPR system included OCR. The design contain image pre-processing, blob segmentation, object detection based on its properties and OCR. Proposed solution use grayscale trasformation, histogram processing, thresholding, connected component,region recognition based on its patern and properties. Implemented is also optical recognition method of licence plate where acquired values are compared with database used to manage entry of vehicles into object.
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Recognition of Vehicle Number Plates
Martinský, Ondrej ; Zbořil, František (referee) ; Zbořil, František (advisor)
This work deals with problematic from field of artificial intelligence, machine vision and neural networks in construction of an automatic number plate recognition system. (ANPR). This problematic includes mathematical principles and algorithms, which ensure a process of number plate detection, processes of proper characters segmentation, normalization and recognition. Work comparatively deals with methods achieving invariance of systems towards image skew, translations and various light conditions during the capture. Work also contains an implementation of a demonstration model, which is able to proceed these functions over a set of snapshots.
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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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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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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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Test form evaluation by OCR
Noghe, Petr ; Fliegel, Karel (referee) ; Kaller, Ondřej (advisor)
This thesis deals with the evaluation forms using optical character recognition. Image processing and methods used for OCR is described in the first part of thesis. In the practical part is created database of sample characters. The chosen method is based on correlation between patterns and recognized characters. The program is designed in a graphical environment MATLAB. Finally, several forms are evaluated and success rate of the proposed program is detected.
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OCR module for recognition of letters and numbers
Kapusta, Ján ; Přinosil, Jiří (referee) ; Sigmund, Milan (advisor)
This paper describes basic methods used for optical character recognition. It explains all procedures of recognition from adjustment of picture, processing, feature extracting to matching algorithms. It compares methods and algorithms for character recognition obtained graphically distorted or else modified image so-called „captcha“, used in present. Further it compares method based on invariant moments and neural network as final classifier and method based on correlation between normals and recognized characters.
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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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License Plate Detection and Recognition for Traffic Analysis
Černá, Tereza ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis describes the design and development of a system for detection and recognition of license plates. The work is divided into three basic parts: licence plates detection, finding of character positions and optical character recognition. To fullfill the goal of this work, a new dataset was taken. It contains 2814 license plates used for training classifiers and 2620 plates to evaluate the success rate of the system. Cascade Classifier was used to train detector of licence plates, which has success rate up to 97.8 %. After that, pozitions of individual characters were searched in detected pozitions of licence plates. If there was no character found, detected pozition was not the licence plate. Success rate of licence plates detection with all the characters found is up to 88.5 %. Character recognition is performed by SVM classifier. The system detects successfully with no errors up to 97.7 % of all licence plates.
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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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