Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Computer analysis of composite documents with non-uniform background.

The motivation behind most of the applications of off-line text recognition is to convert data from conventional media into electronic media. Such applications are bank cheques, security documents and form processing. In this dissertation a document analysis system is presented to transfer gray level composite documents with complex backgrounds and poor illumination into electronic format that is suitable for efficient storage, retrieval and interpretation. The preprocessing stage for the document analysis system requires the conversion of a paper-based document to a digital bit-map representation after optical scanning followed by techniques of thresholding, skew detection, page segmentation and Optical Character Recognition (OCR). The system as a whole operates in a pipeline fashion where each stage or process passes its output to the next stage. The success of each stage guarantees that the operation of the system as a whole with no failures that may reduce the character recognition rate. By designing this document analysis system a new local bi-level threshold selection technique was developed for gray level composite document images with non-uniform background. The algorithm uses statistical and textural feature measures to obtain a feature vector for each pixel from a window of size (2 n + 1) x (2n + 1), where n ≥ 1. These features provide a local understanding of pixels from their neighbourhoods making it easier to classify each pixel into its proper class. A Multi-Layer Perceptron Neural Network is then used to classify each pixel value in the image. The results of thresholding are then passed to the block segmentation stage. The block segmentation technique developed is a feature-based method that uses a Neural Network classifier to automatically segment and classify the image contents into text and halftone images. Finally, the text blocks are passed into a Character Recognition (CR) system to transfer characters into an editable text format and the recognition results were compared to those obtained from a commercial OCR. The OCR system implemented uses pixel distribution as features extracted from different zones of the characters. A correlation classifier is used to recognize the characters. For the application of cheque processing, this system was used to read the special numerals of the optical barcode found in bank cheques. The OCR system uses a fuzzy descriptive feature extraction method with a correlation classifier to recognize these special numerals, which identify the bank institute and provides personal information about the account holder. The new local thresholding scheme was tested on a variety of composite document images with complex backgrounds. The results were very good compared to the results from commercial OCR software. This proposed thresholding technique is not limited to a specific application. It can be used on a variety of document images with complex backgrounds and can be implemented in any document analysis system provided that sufficient training is performed.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A445. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1061. Advisers: Maher Sid-Ahmed; Majid Ahmadi. Thesis (Ph.D.)--University of Windsor (Canada), 2004

IMPROVING THE EFFICIENCY OF TESSERACT OCR ENGINE

This project investigates the principles of optical character recognition used in the Tesseract OCR engine and techniques to improve its efficiency and runtime. Optical character recognition (OCR) method has been used in converting printed text into editable text in various applications over a variety of devices such as Scanners, computers, tablets etc. But now Mobile is taking over the computer in all the domains but OCR still remains one not so conquered field. So programmers need to improve the efficiency of the OCR system to make it run properly on Mobile devices. This paper focuses on improving the Tesseract OCR efficiency for Hindi language to run on Mobile devices as there a not many applications for the same and most of them are either not open source or not for mobile devices. Improving Hindi text extraction will increase Tesseract\u27s performance for Mobile phone apps and in turn will draw developers to contribute towards Hindi OCR . This paper presents a preprocessing technique being applied to the Tesseract Engine to improve the recognition of the characters keeping the runtime low. Hence the system runs smoothly and efficiently on mobile devices(Android) as it does on the bigger machines

Document image analysis and recognition: a survey

This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.The reported study was funded by RFBR, project number 20-17-50177. The authors thank Sc. D. Vladimir L. Arlazarov (FRC CSC RAS), Pavel Bezmaternykh (FRC CSC RAS), Elena Limonova (FRC CSC RAS), Ph. D. Dmitry Polevoy (FRC CSC RAS), Daniil Tropin (LLC “Smart Engines Service”), Yuliya Chernysheva (LLC “Smart Engines Service”), Yuliya Shemyakina (LLC “Smart Engines Service”) for valuable comments and suggestions

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Document image processing using irregular pyramid structure

Ph.DDOCTOR OF PHILOSOPH

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average