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7 Characters Extraction from Strings on a Document Image Using Handwritten Mark on Tablet Shinnosuke Hattori March Introduction Most school classes use paper media such as textbooks and notebooks. We expect that learning method using mobile communications devices such as tablet and smart phone are increasing in the future. But, in the learning system using tablet, the current quality of interactive learning software is not adequate for user demand. To improve the advantage of the interface on mobile devices, we want to extract some character strings specified by marks such as an underline or fence-line from a document image on tablet. In the document image, some users would write marks and notes. We propose a new classification method for handwritten mark and note on a document image. For the extracted mark using underline and fence-line, we develop a new character extraction system from a document image using C language on Windows Mark classification and character extraction methods on a document image 2.1. Mark and note classification method The proposed method consists of three classification criteria. Printing Area criterion is defined by the range of document of printing area and blank area on a document image. The number of stroke criterion is defined by the number of character stroke, as the number of handwritten marks strokes is fewer than that of notes. Object height criterion is defined by the height of the input object because marks are the same height as one text line on the document image. Finally, the system integrates the three criteria degrees and determines whether notes or marks Character extraction method The proposed method extracts a string from handwritten mark using an underline and a fence-line. If the mark is an underline, the system extracts a string from the starting and ending point of the underline. If the mark is a fence-line, the system extracts a string from the coordinate x, y of the fence-line. 3. Evaluation Experiments I implemented the software system, and conducted evaluation experiments of my proposed methods. I used a pen tablet (WACOM DTI-520) and personal computer Mark and note Classification experiment I experimented with 50 notes and 50 marks in document images. Table 1 Mark and note classification results Correct /Total cases Correct rate[%] Note 48/ Mark 41/ Character extraction experiment I experimented with 25 underlines and 25 fence-lines in document images. We defined as success even if over extraction. Table 2 Character extraction results Over extraction rate [%] Positive extraction rate[%] Underline Fence-line Conclusions I proposed a new handwritten mark and note classification method on document images, and character extraction method from the mark. As results of the evaluation experiment, I confirmed that more investigations and improvements are required for practical use. References [1] Bonds-Raacke, Jennifer M., Raacke, John D., Using Tablet PCs in the classroom: an investigation of students' expectations and reactions, Journal of Instructional Psychology, pp , 2008 [2] Ming. Y, Bai, ZS, A Mandarin E-Learning System Based on Speech Recognition and Evaluation, computer applications in engineering education, pp ,

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18 3.1 文書画像の読み込み 本論文で扱う電子テキストは大学授業で用いる教科書をイメージスキャナでスキャンし た画像 (図 3.2[7]) とする ここで 扱うスキャン画像の特徴を以下に示す 文字認識されていない (.bmp 形式,210[dpi]) フォントがある程度統一されている スキャン時の画像の傾きがない 書字方向は横書きのみとする 図 3.2: 実験で使用した電子テキストの例 12

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42 [1] Bonds-Raacke, Jennifer M., Raacke, John D., Using Tablet PCs in the classroom: an investigation of students expectations and reactions, Journal of Instructional Psychology, pp , [2] e 2007 ISS [3] Ming. Y, Bai, ZS, A Mandarin E-Learning System Based on Speech Recognition and Evaluation, COMPUTER APPLICATIONS IN ENGINEERING EDUCATION, pp , [4] [5], PRMU, pp.75-80,2007. [6] :, 14, [7] 2004 C [8] :, 60, 1554, [9]

43 [1] 23, A5-4, 2011 [2] S. Hattori, S. Tsuruoka, H. Takase, and H. Kawanaka Characters Extraction from Strings on a Document Image Using Handwriting Marks on Touch Screen, 10th IAPR International Workshop on Document Analysis Systems (DAS2012), pp.35, 2012 [3] S. Hattori, S. Tsuruoka, H. Kawanaka, and H. Takase Characters Extraction from Strings on a Document Image Using Handwritten Mark on Tablet - Classifying for Handwritten Note and mark -, Proceedings of Fourth International Workshop on Regional Innovation Studies 2012 (IWRIS2012), pp.75-78,

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