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愈发重要的作用。在两大主流二维条形码（2D Barcode）品类中，快速响应码（QR Code）呈指数级扩张态势。QR-DN1.0数据集涵盖覆盖低密度至高密度等级的5类二维码，每类分组包含15个二维码样本：其中5张用于测试集，10张用于训练集。通过盲水印技术（blind watermarking）将上述二维码嵌入30张彩色图像后，采用三种不同方法从手机相机拍摄的图像中提取二维码，最终得到三组共2250张提取后的畸变二维码图像，整体数据集总计包含6750张带噪畸变二维码图像。针对上述三类由不同提取方法生成的数据集，样本均被划分为训练集与测试集两部分：测试集含750张图像，训练集含2250张图像。对于数据集中的每一张畸变二维码样本，均配有对应的无畸变原版二维码作为真值标签（ground truth）。本数据集的一大核心优势在于其真实性：未向图像中添加任何模拟噪声，所有样本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二维码。此外，数据集涵盖多种类型的二维码，包括单字符、短句、长句、统一资源定位符（URL）以及位置信息二维码。