QR-DN1.0: A new Distorted and Noisy QRs dataset

Barcodes are playing a significant role in different industries in the recent years and among the two most popular 2D barcodes, the QR code has grown exponentially. The QR-DN1.0 dataset includes 5 categories of QR codes that will cover low to high density levels. Each group has 15 QR codes: 5 images for testing and 10 images for training. After embedding the QRs into 30 color images using blind watermarking techniques and then extracting the QRs from the images taken with the mobile phone camera with three different methods, we will have three groups of 2250 extracted QR images, which provides a total of 6750 distorted and noisy QR images. In each of the mentioned three categories, the data is divided into two parts: testing, with 750 images, and training, with 2250 images. For every distorted QR in the dataset, a non-distorted instance of it is placed as a ground truth. One of the advantages of this data set is that it is real. Because no simulated noise has been added to the images and this dataset is completely derived from the real word challenge of extracting embedded QRs in color images captured from the watermarked image on the screen. It also includes various types of QRs such as single character, short sentence, long sentence, URL and location.

近年来，条形码在各行业中发挥着关键作用。在两款最主流的二维条形码（2D barcodes）中，QR码（QR code）的应用呈指数级增长态势。QR-DN1.0数据集涵盖5类覆盖低密度至高密度等级的QR码，每类包含15个QR码样本：其中5张用于测试集，10张用于训练集。研究团队先通过盲水印（blind watermarking）技术将这些QR码嵌入30张彩色图像中，随后采用三种不同方法，从手机摄像头拍摄的图像中提取QR码，最终得到三组共2250张提取后的QR图像，总计6750张存在畸变与噪声的QR图像。对于上述提及的三个组别，数据均划分为两部分：测试集含750张图像，训练集含2250张图像。对于数据集中每一张存在畸变的QR码图像，均配有一张对应的无畸变原图作为基准真值（ground truth）。该数据集的一大优势在于其真实性：未对图像添加任何模拟噪声，所有数据均源自真实场景挑战——即从屏幕上的水印图像拍摄的彩色图像中提取嵌入的QR码。此外，数据集涵盖多种类型的QR码，包括单字符、短句、长句、统一资源定位符（URL）以及位置信息等内容。