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예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
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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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‘ ’ “ ” 〔 〕 〈 〉 「 」 『 』 【 】 ＂ （ ） ［ ］ ｛ ｝
± × ÷ ≠ ≤ ≥ ∞ ∴ ♂ ♀ ∠ ⊥ ⌒ ∂ ∇ ≡ ≒ ≪ ≫ √ ∽ ∝ ∵ ∫ ∬ ∈ ∋ ⊆ ⊇ ⊂ ⊃ ∪ ∩ ∧ ∨ ￢ ⇒ ⇔ ∀ ∃ ∮ ∑ ∏ ＋ － ＜ ＝ ＞
、 。 · ‥ … ¨ 〃 ― ∥ ＼ ∼ ´ ～ ˇ ˘ ˝ ˚ ˙ ¸ ˛ ¡ ¿ ː ！ ＇ ， ． ／ ： ； ？ ＾ ＿ ｀ ｜
½ ⅓ ⅔ ¼ ¾ ⅛ ⅜ ⅝ ⅞ ¹ ² ³ ⁴ ⁿ ₁ ₂ ₃ ₄
Æ Ð Ħ IJ Ł Ø Œ Þ Ŧ Ŋ æ đ ð ħ ı ij ĸ ŀ ł ø œ ß þ ŧ ŋ ʼn
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
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      인공 신경망을 이용한 오프라인 한글인식에 관한 연구 = A Study on Off-Line Hangul Recognition Using an Artificial Neural Network

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      https://www.riss.kr/link?id=A30063027

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      Visual recognition of printed and hand-written characters is introduced in context of the neural network paradigm and the results of applying specific neural network architectures are analyzed. Computer classification of character recognition is classified into preprocessing, recognition and techniques which are briefly reviewed in terms of suitability for implementation as a neural network solution for printed and hand-writen character recognition.
      In preprecessing, a single layer perceptron which has 25 processing elements is used for thinning and noise elimination.
      An ART (Adaptive Resonance Theory) model is used in recognition.
      The proposed ART model consists of 1024 PEs in the F1 layer, and 64 PEs in the F2 layer. Recognition results are 82% in character recognition which is independent of writing style.
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      Visual recognition of printed and hand-written characters is introduced in context of the neural network paradigm and the results of applying specific neural network architectures are analyzed. Computer classification of character recognition is class...

      Visual recognition of printed and hand-written characters is introduced in context of the neural network paradigm and the results of applying specific neural network architectures are analyzed. Computer classification of character recognition is classified into preprocessing, recognition and techniques which are briefly reviewed in terms of suitability for implementation as a neural network solution for printed and hand-writen character recognition.
      In preprecessing, a single layer perceptron which has 25 processing elements is used for thinning and noise elimination.
      An ART (Adaptive Resonance Theory) model is used in recognition.
      The proposed ART model consists of 1024 PEs in the F1 layer, and 64 PEs in the F2 layer. Recognition results are 82% in character recognition which is independent of writing style.

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