車牌辨識含以下三大步驟：車牌偵測、字元切割和字元辨識；本研究專注於字元切割與字元辨識之效能提升。影響字元切割常見的因素有光照條件不良而導致的字元破碎或較大視角導致的字元連結。而影響字元辨識常見的原因為相似字元造成混淆，如0與D或8與B等等。本研究提出以MSER (Maximally Stable Extremal Regions, MSER)的特徵擷取得到字元寬度資訊，在字元相連之候選區域進行寬度比對找出最適合的切割邊界，並結合局部二值化處理改良字元破碎的情況，使字元切割可以得到更完整字元候選區域。在解決易混淆之字元的辨識問題上，本研究提出階層式字元分類器，在初始層將易混淆的字元視為同一類別進行群組分類，而接續層專為不同群組而設計，抽取該群組內差異性較大之特徵進行細部分類。上述提出之方法以AOLP (Application Oriented License Plate, AOLP)車牌資料庫進行效能評估，並比較幾種常用的方法，實驗證明本研究所提出的方法具有相當高的競爭力。

Vehicle license plate recognition is generally composed of three modules: license plate detection, character segmentation and character recognition. This research focuses on the improvements of character segmentation and recognition modules. The characters are often wrongly segmented because of shadows, unbalanced or extreme illumination on the plates, or large viewing distances or angles from the cameras. This research applies MSER (Maximally Stable Extremal Region) detector to extract the interest regions among the characters, which are then processed by connected components to determine the best boundary to segment the characters. Given accurately segmented characters, the characters with similar global features are relatively easy to be misclassified, for example, “O” and “D”, and “8” and “B”. A hierarchical classifier is proposed in this research which is composed of a main layer and an enhancement layer. The main layer extracts the global features from all of the alphanumeric characters, and can classify those with distinctive global characteristics. The enhancement layer is only meant for the groups of characters with similar global features by using the group-specific features for more refined classification. The proposed methods are evaluated on the AOLP (Application Oriented License Plate) benchmark database together with a comparison to other competitive methods. Experiments show that the proposed methods can effectively improve the performance of both the character segmentation and recognition modules.

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