在現今的智慧傳輸系統中，車牌辨識是非常重要的一環。車牌辨識廣泛地應用於許多系統上，像是高速公路收費以及停車場管理系統。自2012年起，台灣的交通部公路總局發行了新一代的車牌編碼規則，長度由原本的六碼新增為七碼，且字體也有所更動。為了整合新制車牌與舊制車牌在單一系統上的辨識問題，我們將重新設計一個模型同時兼容兩者，並簡化傳統車牌辨識系統的繁複流程，省去許多影像前處理的動作，來達到相同的效果。
在此篇論文當中，我們基於類神經網路來實作整合六碼及七碼車牌的辨識系統。類神經網路的系統架構及網路訓練使用了Google團隊釋出的開源工具Tensorflow來完成，除了大幅降低以往車牌辨識系統的繁複過程，在辨識準確度及速度都能達到一定水準，此外針對部分遮蔽或是較不清晰的車牌影像，也能夠辨識成功。

License Plate Recognition (LPR) is a very important part of an intelligent transportation system. The LPR is broadly used in many applications, such as highway toll station and car park management. From 2012, Directorate-General of Highway (Taiwan) pursues a new type of license plate, which is changed with the type of font and extra one more digit than existing license plate. In order to solve the recognition on the new license plate (7 digits) and existing license plate (6 digits), we need to redesign a model to made these two types of license plates can be completed in a single system.
In this work, we implement a LPR system based on the neural network to achieve 6 digits and 7 digits’ license plate recognition. The neural network training part is completed with an open source tool, Tensorflow. The results show that our LPR system obtains high accuracy rate even the characters are defaced.

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