by Jian-Zhuang Liu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 183-196). / Microfiche. Ann Arbor, Mich.: UMI, 1998. 3 microfiches ; 11 x 15 cm.

Luo Bo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 55-60). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.vi / Table of Contents --- p.vii / List of Figures --- p.ix / List of Tables --- p.x / List of Abbreviations --- p.xi / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Text in Videos --- p.1 / Chapter 1.3 --- Related Work --- p.4 / Chapter 1.3.1 --- Connected Component Based Methods --- p.4 / Chapter 1.3.2 --- Texture Classification Based Methods --- p.5 / Chapter 1.3.3 --- Edge Detection Based Methods --- p.5 / Chapter 1.3.4 --- Multi-frame Enhancement --- p.7 / Chapter 1.4 --- Our Contribution --- p.9 / Chapter Chapter 2 --- Caption Segmentation --- p.10 / Chapter 2.1 --- Temporal Feature Vectors --- p.10 / Chapter 2.2 --- Principal Component Analysis --- p.14 / Chapter 2.3 --- PCA of Temporal Feature Vectors --- p.16 / Chapter Chapter 3 --- Caption (Dis)Appearance Detection --- p.20 / Chapter 3.1 --- Abstract Image Sequence --- p.20 / Chapter 3.2 --- Abstract Image Refinement --- p.23 / Chapter 3.2.1 --- Refinement One --- p.23 / Chapter 3.2.2 --- Refinement Two --- p.24 / Chapter 3.2.3 --- Discussions --- p.24 / Chapter 3.3 --- Detection of Caption (Dis)Appearance --- p.26 / Chapter Chapter 4 --- System Overview --- p.31 / Chapter 4.1 --- System Implementation --- p.31 / Chapter 4.2 --- Computation of the System --- p.35 / Chapter Chapter 5 --- Experiment Results and Performance Analysis --- p.36 / Chapter 5.1 --- The Gaussian Classifier --- p.36 / Chapter 5.2 --- Training Samples --- p.37 / Chapter 5.3 --- Testing Data --- p.38 / Chapter 5.4 --- Caption (Dis)appearance Detection --- p.38 / Chapter 5.5 --- Caption Segmentation --- p.43 / Chapter 5.6 --- Text Line Extraction --- p.45 / Chapter 5.7 --- Caption Recognition --- p.50 / Chapter Chapter 6 --- Summary --- p.53 / Bibliography --- p.55

No description available.

Automatization is a desirable feature in many business areas. Manually extracting information from a physical object such as a receipt is something that can be automated to save resources for a company or a private person. In this paper the process will be described of combining an already existing OCR engine with a developed python script to achieve data extraction of valuable information from a digital image of a receipt. Values such as VAT, VAT%, date, total-, gross-, and net-cost; will be considered as valuable information. This is a feature that has already been implemented in existing applications. However, the company that I have done this project for are interested in creating their own version. This project is an experiment to see if it is possible to implement such an application using restricted resources. To develop a program that can extract the information mentioned above. In this paper you will be guided though the process of the development of the program. As well as indulging in the mindset, findings and the steps taken to overcome the problems encountered along the way. The program achieved a success rate of 86.6% in extracting the most valuable information: total cost, VAT% and date from a set of 53 receipts originated from 34 separate establishments.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 96-109). Also available on the Internet.

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

No description available.

This thesis describes a new line segmentation method that is optimized for medieval manuscripts. Using a thinned version of the binarized document image, the segmentation algorithm extracts two types of salient features from the handwritten patterns: nodes, whose distribution allows for the detection of line axes; segments, which are labeled according to the nodes they connect. This method obtains very good results on manuscripts that are usually considered hard to segment because of the numerous overlapping and touching lines. By contrast with many existing segmentation algorithms, this method does not rely on user-entered parameters and is not overly sensitive to the quality of the preprocessing treatments. Although more work is required to make it resistant to fluctuating lines, this line separation technique can already handle a large set of medieval documents and provides a useful input to a character segmentation program. / Line segmentation techniques in off-line handwriting recognition -- Line segmentation with the profile method -- Feature-based line segmentation -- Tests and conclusions. / Department of Computer Science

The problem of optical pattern recognition is a broad one. It ranges from identifying shapes in aerial photographs to recognizing letters in hand or machine printed words. This thesis examines many of the issues relating to pattern recognition and, specifically, those pertaining to the optical recognition of characters. It discusses several approaches to various parts of the problem as an illustration of the variety of methods of attack. Some of the particular strengths and weaknesses of those approaches are discussed as well. Finally, a new method of approaching OCR is introduced, developed, and studied. At the conclusion, the study is summarized, the results are examined, and suggestions are made for continued research. / Department of Computer Science