本論文討論圖形辨識系統中常見的一些理論與方法，除了從巨觀的觀點來分析整個圖形辨識系統外，也分別討論各個子系統中常見的方法。圖形辨識系統雖然應用廣泛，不過其精神卻是一致的，整個圖形辨識流程大抵可以分為：特徵表現、特徵選取或特徵粹取、分類器設計以及系統辨識率測試幾個步驟。在特徵表現中，我們會將資料中的特徵量化取出，然後藉由特徵選取或者特徵粹取找出較具分類效果的特徵，藉以降低資料維度，文中我們同時也討論了其他降低資料點的方法，以期能提升辨識速度。接著，我們根據資料是否包含類別資訊以及其他應用考量因素而採用分群法或分類法來進行分類器設計，規範出該圖形辨識系統的分類函數，最後藉由測試資料來驗證該系統的分類函數是否完善，是否需要重新設計分類器，甚至是重新進行特徵選取或特徵粹取以取得更具分類代表性的特徵。
由於圖形辨識是一門涵蓋領域相當廣泛的科學，各式各樣的方法也在不斷被發掘與改良中，根據不同的應用層面，我們無法說有哪一種方法絕對是「最好的」圖形辨識方法，一套優良的圖形辨識系統，除了有賴完整的系統設計流程與測試改良外，更有賴於設計者本身對該應用領域的背景知識是否充足，唯有尋得最適合的特徵與比對方式，才能設計出最佳的圖形辨識系統。

最後，我們也討論了幾種在圖形辨識中常見的加速方法，以期在維持系統的辨識率前提下，也能同時提升系統的辨識速度，縮短系統的反應時間。

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