44 research outputs found

    SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices

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    A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067

    Sparse Convolutional Neural Network for Handwriting Recognition

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2017. 8. ์žฅ๋ณ‘ํƒ.์ž๋™ํ™”๋œ ๋ฌธ์ž ์ธ์‹๊ธฐ๋Š” ์šฐํŽธ๋ฌผ ๋ถ„๋ฅ˜์˜ ์ž๋™ํ™”, ๋ฒˆํ˜ธํŒ ์ธ์‹, ์ „์ž ๋ฉ”๋ชจ์žฅ ๋“ฑ ๋‹ค์–‘ํ•œ ์‚ฐ์—… ๋ถ„์•ผ์—์„œ ๊ทธ ์ˆ˜์š”๊ฐ€ ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ ์ตœ๊ทผ ์ด๋ฏธ์ง€ ์ธ์‹๋ถ„์•ผ์—์„œ ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ์ปจ๋ณผ๋ฃจ์…˜ ์ธ๊ณต ์‹ ๊ฒฝ๋ง(CNN)์„ ์‚ฌ์šฉํ•œ ๋ฐฉ๋ฒ•๋“ค์ด ํ•„๊ธฐ์ฒด ์ธ์‹ ๋ถ„์•ผ์— ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค ๋Œ€๋ถ€๋ถ„์—์„œ๋Š” ๋†’์€ ์ธ์‹๋ฅ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ์ฃผ๋กœ ๊นŠ์€ ๊ตฌ์กฐ์˜ CNN์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ํ•„๊ธฐ์ฒด ์ธ์‹ ๋ถ„์•ผ์—์„œ๋Š” ์ฃผ๋กœ ์Šค๋งˆํŠธํฐ์ด๋‚˜ ํƒœ๋ธ”๋ฆฟ PC ๋“ฑ ์ž์›์ด ์ œํ•œ๋˜์–ด์žˆ๋Š” ๋‹จ๋ง๊ธฐ๊ฐ€ ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋ฏ€๋กœ ๋ชจ๋ธ์ด ์ฐจ์ง€ํ•˜๋Š” ๋ฉ”๋ชจ๋ฆฌ์™€ ๊ณ„์‚ฐ์†๋„ ์—ญ์‹œ ์ค‘์š”ํ•˜๊ฒŒ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด์— ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•™์Šต ๋ณ€์ˆ˜์˜ ์ˆ˜๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ธ์…‰์…˜ ๋ชจ๋“ˆ ๊ธฐ๋ฐ˜์˜ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์„ ํ•œ๊ธ€ ํ•„๊ธฐ์ฒด ์ธ์‹๋ฌธ์ œ์— ์ ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ผ๋ฐ˜ํ™” ์˜ค๋ฅ˜๋ฅผ ๋‚ฎ์ถ”์–ด ์ข€ ๋” ๋†’์€ ์ธ์‹๋ฅ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ๋“œ๋žํ•„ํ„ฐ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์„ ํฌ์†Œํ•œ ์„ฑ์งˆ์„ ๊ฐ€์ง€๋„๋ก ํ•™์Šต์‹œ์ผฐ๋‹ค. ์ธ์…‰์…˜ ๋ชจ๋“ˆ์€ Imagenet Large Scale Visual Recognition Challenge 2014์—์„œ ์ตœ๊ณ ์˜ ์ธ์‹๋ฅ ์„ ๋‹ฌ์„ฑํ•˜๋ฉด์„œ๋„ ๊ธฐ์กด์˜ ๋ชจ๋ธ์— ๋น„ํ•ด 12๋ฐฐ ์ ์€ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํฌ๊ฒŒ ์ฃผ๋ชฉ๋ฐ›์€ GoogLeNet์˜ ํ•ต์‹ฌ ๋ชจ๋“ˆ์ด๋ฉฐ, ๋“œ๋žํ•„ํ„ฐ๋Š” ์ตœ๊ทผ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” regularization ๊ธฐ๋ฒ•์˜ ์ผ์ข…์ธ ๋“œ๋ž์•„์›ƒ์„ CNN์— ์ ํ•ฉํ•˜๊ฒŒ ๋ณ€ํ™”๋ฅผ ์ค€ ๊ธฐ๋ฒ•์ด๋‹ค. ์‹คํ—˜์€ ์šฐ์„  CNN์—์„œ ๋“œ๋žํ•„ํ„ฐ์˜ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ฒŒ ์œ„ํ•ด 10๊ฐœ ํด๋ž˜์Šค, ์ด 60,000์žฅ์˜ ์ž์—ฐ ์ด๋ฏธ์ง€๋กœ ๊ตฌ์„ฑ๋œ Canadian Institute for Advanced Research(CIFAR)-10 ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋“œ๋ž์•„์›ƒ์„ ์ ์šฉํ•œ ๋ชจ๋ธ๊ณผ ์ธ์‹๋ฅ  ๋น„๊ต๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ฒ€์ฆ ์‹คํ—˜์„ ํ†ตํ•ด ๋“œ๋žํ•„ํ„ฐ ๊ธฐ๋ฒ•์ด CNN์— ์ ์šฉ๋˜์—ˆ์„ ๋•Œ ๋“œ๋ž์•„์›ƒ๋ณด๋‹ค ์ผ๋ฐ˜ํ™” ์˜ค๋ฅ˜๋ฅผ ๋‚ฎ์ถ”๋Š”๋ฐ ๋” ๋›ฐ์–ด๋‚จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๊ฒ€์ฆ ์‹คํ—˜ ์ค‘ ๊ฐ ์€๋‹‰ ์ธต๋งˆ๋‹ค ๋“œ๋žํ•„ํ„ฐ์˜ ํšจ๊ณผ๊ฐ€ ๋‹ค๋ฅด๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ ์ธ ๊ฒ€์ฆ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ดํ›„ ๋“œ๋žํ•„ํ„ฐ๋ฅผ ์ธ์…‰์…˜ ๋ชจ๋“ˆ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ตฌ์„ฑ๋œ CNN์— ์ ์šฉํ•œ ๋’ค ํ•œ๊ธ€ ํ•„๊ธฐ์ฒด ์ธ์‹์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜์— ์‚ฌ์šฉํ•œ ๋ฐ์ดํ„ฐ๋Š” ์ด 520ํด๋ž˜์Šค, 260,000 ๊ธ€์ž์˜ ํ•œ๊ธ€ ๋‚ฑ๊ธ€์ž๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค. ํ•œ๊ธ€ ํ•„๊ธฐ์ฒด ์ธ์‹ ์‹คํ—˜ ๊ฒฐ๊ณผ ์ œ์•ˆํ•˜๋Š” ๋ชจ๋ธ์ธ ๋“œ๋žํ•„ํ„ฐ๋ฅผ ์ ์šฉํ•œ ์ธ์…‰์…˜ ๋ชจ๋“ˆ ๊ธฐ๋ฐ˜์˜ CNN์ด ๊ธฐ์กด์˜ LeNet ๊ตฌ์กฐ์˜ CNN์— ๋น„ํ•ด 3๋ฐฐ ๋” ์ ์€ ํ•™์Šต๋ณ€์ˆ˜๋กœ๋„ 3.279% ๋†’์€ ์ธ์‹๋ฅ ์„ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค.I. ์„œ ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ ๋ฌธ์ œ 5 II. ๊ด€๋ จ ์—ฐ๊ตฌ 6 1. ์ปจ๋ณผ๋ฃจ์…˜ ์ธ๊ณต ์‹ ๊ฒฝ๋ง 6 1.1. ์ปจ๋ณผ๋ฃจ์…˜ ์—ฐ์‚ฐ์˜ ์ •์˜ 6 1.2. ์ปจ๋ณผ๋ฃจ์…˜ ์ธ๊ณต ์‹ ๊ฒฝ๋ง 7 2. ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ํ•œ๊ธ€ ํ•„๊ธฐ์ฒด ์ธ์‹ 8 3. ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์˜ ๋‹ค์–‘ํ•œ ๊ตฌ์กฐ 8 3.1. Residual Network ๊ตฌ์กฐ 9 3.2. GoogLeNet ๊ตฌ์กฐ 10 4. ์ธ๊ณต ์‹ ๊ฒฝ๋ง์˜ Regularization 12 4.1. ๋‹ค์ธต ํผ์…‰ํŠธ๋ก ์—์„œ์˜ Regularization 12 4.2. ์ปจ๋ณผ๋ฃจ์…˜ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์—์„œ์˜ Regularization 12 III. ์ œ์•ˆํ•˜๋Š” ๋ชจ๋ธ 14 1. ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์—์„œ์˜ ๋“œ๋ž์•„์›ƒ 14 2. ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์—์„œ์˜ ๋“œ๋žํ•„ํ„ฐ 17 3. ๋“œ๋žํ•„ํ„ฐ๊ฐ€ ์ ์šฉ๋œ ์ธ์…‰์…˜ ๋ชจ๋“ˆ 20 IV. ์‹คํ—˜ ๋ฐ ํ•„๊ธฐ์ฒด ์ธ์‹ ๊ฒฐ๊ณผ ๋ถ„์„ 21 1. ๋ฐ์ดํ„ฐ ๋ช…์„ธ 21 2. ๋“œ๋žํ•„ํ„ฐ์˜ ํšจ๊ณผ ๋ถ„์„ 23 3. ํ•„๊ธฐ์ฒด ์ธ์‹ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 28 4. ๊ธฐํƒ€ ๋…ผ์˜์‚ฌํ•ญ 32 V. ๊ฒฐ ๋ก  33 ์ฐธ๊ณ ๋ฌธํ—Œ 34 ์˜๋ฌธ์š”์•ฝ 38Maste

    A character-recognition system for Hangeul

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    This work presents a rule-based character-recognition system for the Korean script, Hangeul. An input raster image representing one Korean character (Hangeul syllable) is thinned down to a skeleton, and the individual lines extracted. The lines, along with information on how they are interconnected, are translated into a set of hierarchical graphs, which can be easily traversed and compared with a set of reference structures represented in the same way. Hangeul consists of consonant and vowel graphemes, which are combined into blocks representing syllables. Each reference structure describes one possible variant of such a grapheme. The reference structures that best match the structures found in the input are combined to form a full Hangeul syllable. Testing all of the 11 172 possible characters, each rendered as a 200-pixel-squared raster image using the gothic font AppleGothic Regular, had a recognition accuracy of 80.6 percent. No separation logic exists to be able to handle characters whose graphemes are overlapping or conjoined; with such characters removed from the set, thereby reducing the total number of characters to 9 352, an accuracy of 96.3 percent was reached. Hand-written characters were also recognised, to a certain degree. The work shows that it is possible to create a workable character-recognition system with reasonably simple means

    Arabic Handwriting: Analysis and Synthesis

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    Multi-domain sketch understanding

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 121-128).by Christine J. Alvarado.Ph.D
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