The subject of machine recognition of connected characters is investigated. A generic single character recognizer (SCR) assumes there is only one character in the image. The goal of this project is to design a connected character segmentation algorithm (CCSA) without the above assumption. The newly designed CCSA will make use of a readily available SCR. The input image (e.g. a word with touching letters) is first transformed (thinned) into its skeletal form. The CCSA will then extract the image features (nodes and branches) and store them in a hierarchical form. The hierarchy stems from the left-to-right rule of writing of the English language. The CCSA will first attempt to recognize the first letter. When this is done, the first letter is deleted and the algorithm repeats. After extracting the image features, the CCSA starts to create a set of test images from the beginning of the word (i.e. beginning of the description). Each test image contains one more feature than its predecessor. The number of test images in the set is constrained by a predetermined fixed width or a fixed total number of features. The SCR is then called to examine each test image. The recognizable test image(s) in the set are extracted. Let each recognizable test image be denoted by C₁. For each C₁, a string of letters C₂, C₃, CL is formed. C₂ is the best recognized test image in a set of test images created after the deletion of C₁ from the beginning of the current word. C₃ through CL are created by the same method. All such strings are examined to determine which string contains the best recognized C₁. Experimental results on test images with two characters yield a recognition rate of 72.66%. Examples with more than two characters are also shown. Furthermore, the experimental results suggested that topologically simple test images can be more difficult to recognize than those which are topologically more complex. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Master of Science

No description available.

Thesis (PhD (Mathematical Sciences))--University of Stellenbosch, 2005. / A great deal of work has been done in the area of off-line signature verification over the past two decades. Off-line systems are of interest in scenarios where only hard copies of signatures are available, especially where a large number of documents need to be authenticated. This dissertation is inspired by, amongst other things, the potential financial benefits that the automatic clearing of cheques will have for the banking industry.

M.Com. (Informatics) / Please refer to full text to view abstract

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 365-385. / by Theodore Thomas Kuklinski. / Ph.D.

by Lin Feng. / "June 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. [114]-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

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

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