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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Access type: Metadata onlyAuthor: search.filters.author.Tseng, YH

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    Spectral properties of Eu3+-activated yttrium oxysulfide red phosphor
    (1998-09-30)
    Tseng, YH
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    Chiou, BS
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    Peng, CC
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    Ozawa, L
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    電子工程學系及電子研究所
    The effects of Eu on the spectral properties of Y2O2S have been investigated. The Y2O2S:Eu phosphor powders were prepared with a flux fusion method and electrophoretically deposited on an ITO-coated glass substrate to form a thin layer. Both cathodoluminescence and photoluminescence emission spectra of Y2O2S:Eu were measured. The highly intense emission lines at 616 and 626 nm are attributed to the transition from D-5(0) to F-7(2) levels. The color of Y2O2S:Eu phosphor varies from orange to red as the Eu concentration increases from Eu/ Y = 0.025 (molar ratio) to 0.06. The luminescence decay times are shorter than 1.1 ms for Eu concentrations ranging from 0.025 to 0.06 (Eu/ Y molar ratio). The luminescent color becomes redder but less bright as the Eu concentration is increased. (C) 1998 Elsevier Science S.A. All rights reserved.
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    Typeface identification for printed Chinese characters
    (1998-03-01)
    Tseng, YH
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    Kuo, CC
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    Lee, HJ
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    資訊工程學系
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    Department of Computer Science
    In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese characters are considered, and a trapezoid-shaped membership function is constructed for each distribution. Based on these membership functions, we determine what typeface each input character belongs to using a two-level decision tree. To increase the identification rate, the typeface of a certain character is adjusted according to the typeface identification results of the front and the next characters. In the character recognition system, we use two statistical features: crossing counts and contour directional counts. We achieved an 89.87% typeface identification rate in our experiments, and a 95.60% character recognition rate.
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    Speeding-up Chinese character recognition in an automatic document reading system
    (1997)
    Tseng, YH
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    Kuo, CC
    ;
    Lee, HJ
    ;
    交大名義發表
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    資訊工程學系
    In this paper, we present two techniques for speeding up character recognition. Our character recognition system, including the candidate-cluster selection and detail-matching modules, is implemented using two statistical features: crossing-counts and contour-direction counts. In the training stage, we divide characters into different clusters. To keep very high recognition rate, the candidate-cluster selection module selects the top 60 clusters with minimal distances from among 300 predefined clusters. To further speed-up the recognition speed, we use a modified branch-and-bound algorithm in the detail-matching module. In the automatic document-reading system, characters and punctuation marks are first extracted from printed document images and sorted according to their positions and the document orientation. The system then recognizes all printed Chinese characters between pairs of punctuation marks. The results are then spoken aloud by a speech-synthesis system.
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    Interfered-character recognition by removing interfering-lines and adjusting feature weights
    (1998)
    Tseng, YH
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    Lee, HJ
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Characters sometimes overlap with non-textual lines in form documents and these interfered-characters would generally be recognized with poor accuracy. In this paper we propose a two-step interfering-line removing method. Positions and orientations of interfering-lines are first detected by the Hough transform. Interfering-line widths are then determined from projection histograms. An ambiguous area is defined to bound an interfering-line. Black runs in the ambiguous are classified into four types and grouped into run-groups. The directions of hidden character strokes in each run-groups are predicted. Black pixels located in these hidden strokes are regarded as character pixels and the other black pixels are considered as interfering pixels, which will be removed then. Most OCR engines are trained by non-interfered sample characters. In order to recognize interfered-characters, we adjust the feature values by assigning a stability value to each of sub-regions. In our collected 1820 interfered handwritten Chinese characters, the recognition accuracy was 24.02% for interfered-characters and 89.91% for characters after removing interfering-lines.