影像處理及類神經乏晰於車輛牌照自動辨識之應用研究

A Hybrid Approach for Automatic Vehicle License Plate Recognition

仲崇實(Chung-Shih Jong)

指導教授 ： 李 錫 捷

元智大學/管理學院/資訊研究所/碩士(1997年)

摘要

一般傳統的車輛辨識系統，多半使用了R.F.、microwave或紅外線等技術來進行辨認，並且透過在車輛上安裝的transponder，將資料傳送給感測設備。這樣的系統在實施時，必須顧慮到transponder必須安裝在所有車輛上的成本問題以及其故障的可能性。

在本研究中，我們提出了一個混合方法，用以對於車輛牌照進行自動的辨認，並且建立了一個雛形系統，可以獨立作業或是與其他現有的車輛辨識系統合作處理。本系統大致可以分成四個主要的模組，它們分別是：牌照定位模組－負責由影像中找出概略的牌照位置、字元擷取與正規化模組－由牌照影像中，取出各個字元，並且正規化、辨識模組－專司辨識各個已經分離出來的字元，以及SimNet類神經－乏晰系統模組。

為了驗證此系統的效能，我們以數位照相機蒐集了417張影像，經由實驗結果得知，本系統的牌照正確率約在93.5%，而字元辨識的正確率為97.3%。

關鍵字

汽車牌照自動辨識 ； 樣式辨認 ； 光學字元閱讀 ； 類神經網路

並列摘要

Most currently available vehicle identification systems use techniques such as R. F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory systems. It is considered expensive to install a transponder in each vehicle and malfunction of the transponder will result in the failure of the vehicle identification system.

In this study, a hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for pattern recognition using SimNet. To test the performance of the proposed system, 417 vehicle image samples are using by a digital camera. The license plate recognition success rate of the prototype is 93.5% while the character recognition success rate of the prototype is 97.3%

並列關鍵字

Vehicle License Plate Recognition ； Pattern Recognition ； OCR ； Neural Network

參考文獻

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