QR碼（quick response code）是一種二維條碼，利用里德-所羅門碼（Reed-Solomon code）進行編碼，它的特點是有可以快速編解碼的特性，隨著智慧型裝置的普及和相機功能的進步，已經成為在日常生活中，傳遞資訊的常用方式，其應用範圍廣泛，如紙本海報、紙本票卷、紙本發票等。尤其在近幾年隨著COVID-19開始蔓延後，QR code開始也被用作非接觸式系統來傳遞信息，如被政府用於記錄行蹤等。
然而QR碼在使用時，常見是以紙張作為載台，印刷出QR碼後，作為主要的使用方式，由於紙張在使用時，容易受到彎曲或是摺痕的影響，又或是在鏡頭拍攝紙本QR碼時，因拍攝角度問題，造成QR碼變形的情況，這兩種情況都會讓紙本QR碼影像產生變形，導致QR碼解碼器產生無法成功解碼的情形出現。
所以本論文提出一種利用深度學習的技術，去處理上述的變形QR碼的方法，該方法目的在透過卷積神經網路方式恢復紙本變形QR碼影像，使其恢復至QR碼解碼器可以成功解碼的狀態。另外，因紙本變形QR碼影像，難以透過現實手段，大量獲取，所以本論文透過程式模擬的方式，產生卷積神經網路模型所需訓練的資料。
最後，通過實驗比較不同版本大小QR碼，使用本文方式恢復的QR碼解碼成功率，結果顯示，深度學習能夠成功恢復紙本變形QR碼，提高解碼器的解碼率。

QR code (quick response code) is a type of two-dimensional barcode that utilizes Reed-Solomon code for encoding. Its characteristic feature is the ability to quickly encode and decode information. With the widespread use of smart devices and advancements in camera functionality, QR codes have become a common method of information transmission in daily life. They have a wide range of applications, such as paper posters, paper tickets, and paper receipts. Especially in recent years, with the spread of COVID-19, QR codes have also been used as contactless systems for information transmission, including being utilized by governments to track movement.
However, when using QR codes, it is common to use paper as the carrier. After printing the QR codes on paper, it becomes the primary mode of usage. Due to the nature of paper, it is prone to bending, folding, or distortion when captured by a camera from various angles. These situations can cause the QR code image on paper to deform, resulting in unsuccessful decoding by QR code readers.
Therefore, this paper proposes a method that uses Deep Learning techniques to address the issue of deformed QR codes mentioned above. The purpose of this method is to restore the deformed QR codes image on paper using convolutional neural networks (CNN), making it possible for QR codes readers to successfully decode it. Additionally, since it is difficult to obtain a large amount of deformed QR code images on paper through real-world means, we generate the training data for the CNN model through program simulation.
Finally, by conducting experiments and comparing different versions, sizes, and error correction capabilities of QR codes, as well as the success rate of decoding QR codes restored using CNN, the results demonstrate that CNN can successfully restore deformed QR codes on paper and improve the decoding rate of QR code readers.

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