臺灣博碩士論文加值系統

車牌辨識系統經過多年發展，目前已有不少商業應用，但主要是以定置型電腦為計算平台，如停車場出入管理等相關應用。本論文提出一種適於可攜式影像擷取裝置之車牌辨識演算法，使得車牌辨識的實現更有彈性。此演算法在硬體資源有所限制的環境下，可達到八成以上的辨識率及單一車牌約兩秒的執行時間。
本論文所提出之車牌辨識演算法實作於定點式數位訊號處理器上，包含車牌定位、字元切割及字元辨識等三個階段。車牌定位步驟利用影像輪廓及亮度特徵來進行定位，並利用影像縮圖提升計算速度；字元切割步驟則先以複合式車牌水平度校正方法進行處理，提升切割及後續步驟的正確率；字元辨識步驟使用階層式單層類神經網路來進行辨識，藉此解決相似字型辨識問題。
本研究在DSP開發板上實現本論文所提出之LPR演算法。經過實驗，本論文平均辨識率為87.2%。上述結果是基於539張影像所組成的樣本空間，其中每張影像均包含一個或一個以上的車牌，採用不同的拍攝角度，並且在不同的天氣及亮度條件之下。根據上述初步的實驗結果可以得知，本論文所提出之LPR演算法具有相當的潛力，可將一般常見附照相功能的可攜式裝置提升為具有車牌辨識能力的設備，並具有合理的辨識率。

Automatic license plate recognition has attracted many researchers’ attention in recent years, and their efforts have resulted in many installed business applications. However, most existing installations are non-moving, such as automatic parking control and payment systems. In order to provide flexibility for implementation, this thesis develops a set of algorithms suitable for realizing license plate recognition (LPR) capability on handheld mobile devices. The general design goal is for a handheld device with limited hardware resources to be able to read a license plate from a picture containing a single license plate image within two seconds and with 80% or higher rate of successful recognition.

The proposed LPR algorithm consists of three major steps, namely, license plate location, character segmentation, and character recognition. Plate location is achieved mainly by extracting image outlines of probable areas. For pictures of acceptable quality, two-dimensionally reduced images are used to save processing time. To separate the characters with higher accuracy, the character segmentation step applies the techniques of hybrid rotation correction. Finally, an one-layer artificial neural network is deployed for character recognition.

We have implemented the proposed LPR algorithm on a DSP-based development board. The experimental results show that, the total average rate of successful recognition is 87.2%. This is based on a sample space of 539 pictures including the ones containing more than a single license plate, taken with various shooting angles and under a range of weather and light conditions. This preliminary result also shows that our proposed LPR algorithm has the potential to give today’s many picture-taking mobile devices the ability to recognize license plates with reasonably high recognition rate.

致謝…………………………………………………………………………I
摘要………………………………………………………………………..II
Abstract………………………………………………………………….IV
目錄……………………………………………………………………….VI
圖目錄…………………………………………………………………….IX
表目錄………………………………………………………………… ..XII
第一章 緒論……………………………………………………………...1
1.1 研究動機……………………………………………………….....1
1.2 相關研究概述………………………………………………….....2
1.3 論文架構……………………………………………………………..3
第二章 車牌辨識系統……..………………….…………………………4
2.1 系統概要…………………………………………………………....4
2.2基本影像操作方式與資料結構…………………………………….5
2.2.1座標記錄串列與影像儲存串列………………………………..6
2.2.2連接物件編碼方式之一………………………………………10
2.2.3連接物件編碼方式之二………………………………………13
2.3 系統流程與簡介…………………………………………………..15
第三章 車牌定位……………………………….……………….....19
3.1 車牌特徵擷取……………………………………………………..19
3.1.1影像輪廓擷取…………………………………………………19
3.1.2輪廓密集度判定………………………………………………22
3.1.3灰階影像二值化………………………………………………24
3.2車牌影像定位第一階段…………………………………………...25
3.2.1縮圖定位………………………………………………………26
3.2.2縮圖定位詳細步驟……………………………………………27
3.3車牌影像定位第二階段…………………………………………...31
3.3.1第二階段定位詳細步驟………………………………………31
3.3.2其他背景雜訊消除運算………………………………………35
第四章 車牌字元切割與辨識………………………………………….37
4.1 字元連接物件擷取………………………………………………..37
4.1.1字元連接物件擷取方式及特徵………………………………37
4.1.2字元連接物件相關操作應用…………………………………40
4.2車牌傾斜度校正…………………………………………………...43
4.2.1傾斜度校正運算………………………………………………43
4.2.2複合式傾斜度校正機制………………………………………45
4.3 字元切割步驟……………………………………………………..48
4.4 字元辨識步驟……………………………………………………..53
4.4.1字元辨識步驟流程簡介………………………………………53
4.4.2相似字元調整…………………………………………………56
第五章 實驗與效能分析……………………………………………….59
5.1 實驗環境……………………………………………………......59
5.1.1實驗設備………………………………………………………59
5.1.2測試樣本環境…………………………………………………60
5.2 實驗結果……….………………………………………………….62
5.2.1車牌定位步驟結果……………………………………………62
5.2.2字元切割步驟結果……………………………………………64
5.2.3字元辨識步驟結果……………………………………………67
5.3 整體執行效能分析………………………………………………69
第六章 結論與未來發展…………………………………….......72
參考文獻………………………………………………………………….73

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