隨著經濟持續發展，為了生活上的必須而購買汽車的人數不斷增加，基於這個理由，自動化的車牌辨識技術便是實現智慧型運輸系統（Intelligent Transport System, ITS）的一項關鍵性功能。
我們所發展的車牌辨識系統可適用於台灣各種車輛的車牌，整個系統分為三大部分，第一是利用同心視窗掃描法（Sliding Concentric Windows, SCW）從影像中偵測出車輛的車牌區域，第二步驟則是用連通物件表示法（Connected-Component Labeling）切割出車牌上的每個字元，最後交給使用機率類神經網路（Probabilistic Neural Networks, PNN）來決定影像中的字元類別，並針對容易混淆的字元設計了兩層進一步的辨識處理（2-layer Ambiguous Character Handling）。
本系統在1095張車輛影像以及500段停車場出入影片中，對車牌有接近98%的辨識率，針對字元而言則有99%的高辨識率。

Due to the development of economy, more and more people buy cars for living. For this reason, automatic license plate recognition technology is a critical function in the application of the Intelligent Transport System (ITS).
The license plate recognition system we develop is applicable to all kinds of license plates in Taiwan; the whole system consists of three parts, firstly, using Sliding Concentric Windows (SCW) to detect the region of license plate in the image, secondly, the connected-component labeling is used for segmenting each character in the license plate, and finally the characters are decided by a Probabilistic Neural Networks (PNN). And for the ambiguous characters, a 2-layer approach is used for further recognition.
In 1095 cars and 500 image sequences, the recognition rate for license plate and characters are 98% and 99%, respectively.

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