本系統主旨為建置一個適合台灣環境使用之車牌辨識系統，必須要能濾除複雜背景、快速多標偵測汽車與機車車牌、校正車牌、車牌辨識。因此本系統分為四個部分:影像前處理、車牌偵測、車牌正規化、車牌辨識。
影像前處理部分本系統在能增加系統準確率與不增加處理時間之前提下利用車牌特有之色彩特徵提出色彩差異度測邊演算法結合Sobel橫向測邊而能夠濾除影像中約62.5%非車牌之邊點。車牌偵測部分尋找影像邊點中符合字元特性者再予以形態學處理找出車牌候選區，並在經由密度濾波與物件特性等特點找出實際車牌區域，此法可於單次掃描中找出影像中所有車牌，且不限於車牌配色，準確率高達96.41%，花費時間為168ms。車牌正規化部分為解決車牌具有歪斜或歪曲等不理想狀況，也為提升車牌辨識率所必須處理之過程，本文使用車牌字元斜度估計與歪曲車牌校正法，可校正之車牌為歪斜0°~36°，鑑於實際情況中歪斜超過30°之車牌極其罕見故為可接受之範圍。車牌辨識部分為增加字元辨識率，本系統使用投影法與切割區域估測法準確找出車牌中文字沾黏之部分再予以切割，且本系統使用機率模板文字比對法，比對字元每個像素值接有不同權重且執行速度快準確率也高達97.89%。最終本系統所達成之車牌辨識率為89.16%，平均系統花費時間為241ms。比較於現有其它已發表論文之偵測與辨識系統表現，本系統所受限制少且準確率較高。

This system is specially built for Taiwan’s environment. It has 4 parts: image preprocessing, multiple license-plate detection, normalization and recognition.
In image preprocessing, this system takes the benefit of license plate’s color feature then come out a color differential edge detection (CDED) algorithm combines with horizontal Sobel mask, which can erase 62.5% of uninteresting pixels. In detection part, finding edge pixel of character feature then using morphology and density filter extracts the real license plate region. This method can find all license plates region in one scan and has 96.41% succession rate therefore consuming time is 168ms. Normalization part has to solve license plate’s rotation and warp to increase recognition succession rate. This system uses characters in license-plate to estimation rotation angle, and can normalize angle of 0°~36°. In recognition part, projection segmentation combines with segmentation region estimation method can segment sticky characters and increase recognition rate. Finally this system uses probability templates as OCR which has weight for each pixel for every character, and it is fast and has 97.89% succession rate. Of all, this System’s recognition rate is 89.16% and consuming time is 241ms. This paper also collects nowadays LPR related papers, and this system has less restricts and higher succession rate than those.

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