Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/72819
標題: Design+and+Implementation+of+Intelligent+Optical+Character+Recognition+System+by+Functional-Link+Neural+Network
智慧型字元辨識系統設計與實現:使用基於函數鏈結類神經網路
作者: Lee, Ching-Hung
李慶鴻
Liu, Hsin-Yi
Lee, Chung-Kai
劉欣宜
李仲凱
關鍵字: 字元辨識
optical character recognition
特徵抽取
類神經網路
影像處理
feature extraction
neural network
image processing
出版社: 國立中興大學工學院;Airiti Press Inc.
摘要: In this study, a functional-link-based neural network (FLNN) is adopted to develop an optical character recognition (OCR) system. The proposed OCR system contains the feature extraction and the FLNN classifier. The OCR system is valid for recognizing the capital letters and Arabic numerals. The proposed feature extraction technique includes the distance between each black pixel and concentric circles feature. They have the properties of rotation and noise invariant. In addition, the proposed FLNN with the expanded functional-basis has the properties of anti-noise and distortion characteristics. Finally, some experimental results are introduced to illustrate the performance and effectiveness of the proposed feature extraction and FLNN classifier. The proposed system performs 100% accuracy for actual characters and at least 94% even the characters has variations of location, size, rotation, and noisy. For different fonts (Arial and Time New Roman), the proposed system almost perform well and has 80% accuracy.
本文以函數鏈結類神經網路(functional-link-based neural network, FLNN)設計智慧型字元辨識器,辨識系統包括字元特徵值擷取與FLNN分類器。函數鏈結類神經網路以函數鏈結擴充為基底,且擁有抗雜訊和容失真的特性,在隱藏層節點個數較少的情況下,也能擁有很好的效能。本文提出重心距離特徵值和同心圓面積特徵值,其皆具有旋轉不變性以及抗雜訊的特性,適用於理想及旋轉、具雜訊字元之分類。在分類理想字元,辨識正確率為100%;分類非理想字元(具字元位置及大小、具雜訊和旋轉角度之誤差),辨識正確率94%~100%;而對於不同字型之分類(Arial與Times New Roman),辨識正確率亦維持80%以上,這些實驗均可說明所設計字元辨識器之可行性與性能。
URI: http://hdl.handle.net/11455/72819
ISSN: 1017-4397
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10174397-201211-201212060037-201212060037-145-154
Appears in Collections:第23卷 第3期
工學院

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