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Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω α β γ δ ε ζ η θ ι κ λ μ ν ξ ο π ρ σ τ υ φ χ ψ ω
á à Á À é è É È ç Ç ê
Ä Ö Ü ä ö ü ß
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‘ ’ “ ” 〔 〕 〈 〉 「 」 『 』 【 】 ＂ （ ） ［ ］ ｛ ｝
± × ÷ ≠ ≤ ≥ ∞ ∴ ♂ ♀ ∠ ⊥ ⌒ ∂ ∇ ≡ ≒ ≪ ≫ √ ∽ ∝ ∵ ∫ ∬ ∈ ∋ ⊆ ⊇ ⊂ ⊃ ∪ ∩ ∧ ∨ ￢ ⇒ ⇔ ∀ ∃ ∮ ∑ ∏ ＋ － ＜ ＝ ＞
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½ ⅓ ⅔ ¼ ¾ ⅛ ⅜ ⅝ ⅞ ¹ ² ³ ⁴ ⁿ ₁ ₂ ₃ ₄
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А Б В Г Д Е Ё Ж З И Й К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Ъ Ы Ь Э Ю Я а б в г д е ё ж з и й к л м н о п р с т у ф х ц ч ш щ ъ ы ь э ю я
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§ ※ ☆ ★ ○ ● ◎ ◇ ◆ □ ■ △ ▽ → ← ↑ ↓ ↔ 〓 ◁ ◀ ▷ ▶ ♤ ♠ ♡ ♥ ♧ ♣ ⊙ ◈ ▣ ◐ ◑ ▒ ▤ ▥ ▨ ▧ ▦ ▩ ♨ ☏ ☎ ☜ ☞ † ‡ ↕ ↗ ↙ ↖ ↘ ♭ ♩ ♪ ♬ ㉿ ㈜ № ㏇ ™ ㏂ ㏘ ℡ ＃ ＆ ＊ ＠ ª º
ⅰ ⅱ ⅲ ⅳ ⅴ ⅵ ⅶ ⅷ ⅸ ⅹ Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ
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      NEIS 평가시스템 기능 향상을 위한 필기체 한글 인식 = Handwriting Hangul Recognition for Improvement of Evaluation System on NEIS

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      https://www.riss.kr/link?id=A82691060

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      다국어 초록 (Multilingual Abstract)

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      This research aims to develop the Korean handwriting recognition method with the help of Backpropagation Neural Network Learning Method, apply the method to the evaluation system, and improve the problems derived from the multiple choice type test. When the short written answers were scored, every syllable was divided into graphemes and strokes were extracted. Extracted strokes were presented according to the probability of their appearance, and then trained with six Type Recognition Neural Network. They were trained and recognized again with the specific neural network. After trained by PE92, the certified DB, and recognized by using the result of the subjective answers, the recognition ratio was 79.2%. This ratio is higher than the ratio in previous research.
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      This research aims to develop the Korean handwriting recognition method with the help of Backpropagation Neural Network Learning Method, apply the method to the evaluation system, and improve the problems derived from the multiple choice type test. Wh...

      This research aims to develop the Korean handwriting recognition method with the help of Backpropagation Neural Network Learning Method, apply the method to the evaluation system, and improve the problems derived from the multiple choice type test. When the short written answers were scored, every syllable was divided into graphemes and strokes were extracted. Extracted strokes were presented according to the probability of their appearance, and then trained with six Type Recognition Neural Network. They were trained and recognized again with the specific neural network. After trained by PE92, the certified DB, and recognized by using the result of the subjective answers, the recognition ratio was 79.2%. This ratio is higher than the ratio in previous research.

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      목차 (Table of Contents)

      • Ⅰ. 서론 1
      • Ⅱ. 필기체 한글인식 2
      • 1. BP 알고리즘 2
      • 2. OMR카드 설계 2
      • 3. 제안한 객관식 채점 2
      • Ⅰ. 서론 1
      • Ⅱ. 필기체 한글인식 2
      • 1. BP 알고리즘 2
      • 2. OMR카드 설계 2
      • 3. 제안한 객관식 채점 2
      • 4. 제안한 주관식 채점 3
      • Ⅲ. 실험 및 결과 4
      • Ⅳ. 결론 5
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