27 research outputs found

    Advances in Character Recognition

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    This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

    Offline printed Sindhi optical text recognition: survey

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    Optical Charter Recognition (OCR) applications are becoming more intensive than before and show great prospective for rapid data entry, but has limited success when applied to the Sindhi language. This paper summarize the general topic of optical character recognition and highlights the characteristics of Sindhi script. It also presents an historical review of the Sindhi text recognition systems. More this paper underlines the capabilities of different OCT=R systems, and then introduce a five stage model for off-line printed Sindhi text recognition system and classify research work according to this mode

    Chinese information processing

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    A survey of the field of Chinese information processing is provided. It covers the following areas: the Chinese writing system, several popular Chinese encoding schemes and code conversions, Chinese keyboard entry methods, Chinese fonts, Chinese operating systems, basic Chinese computing techniques and applications

    A Font Search Engine for Large Font Databases

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    A search engine for font recognition is presented and evaluated. The intended usage is the search in very large font databases. The input to the search engine is an image of a text line, and the output is the name of the font used when rendering the text. After pre-processing and segmentation of the input image, a local approach is used, where features are calculated for individual characters. The method is based on eigenimages calculated from edge filtered character images, which enables compact feature vectors that can be computed rapidly. In this study the database contains 2763 different fonts for the English alphabet. To resemble a real life situation, the proposed method is evaluated with printed and scanned text lines and character images. Our evaluation shows that for 99.1% of the queries, the correct font name can be found within the five best matches
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