Abstract
Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.
Highlights
Quick Response (QR) codes are widely used for tracking labeled industrial and commercial products, advertising and marketing, sale of goods, identification of business cards, bank accounts, immigration stamps, post mailing, virtual store, and entertainment and, in general, in many situations, where sharing information about any object is required
This paper presents some advances for expanding traditional approaches for QR code detection in real-time applications
The conceptual contribution of this paper consists in development of approach for precise and fast recognition of single and multiple QR codes in images captured in uncontrolled environments, where medium to strong blurring and noising, significant nonuniform illumination, and considerable symbol deformations are presented
Summary
Quick Response (QR) codes are widely used for tracking labeled industrial and commercial products, advertising and marketing, sale of goods, identification of business cards, bank accounts, immigration stamps, post mailing, virtual store, and entertainment and, in general, in many situations, where sharing information about any object is required. With the development of wearable and ubiquitous smartphones, QR code scanners are available anytime and anywhere providing quick high density data storage and retrieval of data, which is based on the arrangement of multiple simple geometric shapes over a fixed space It is very cheap, reliable, and secure way to include information on objects that can be exploited by augmented and mixed reality systems with low requirements to their computational power. Since February 2015, the QR code is regulated by ISO/IEC 18004:2015 standard clarifying previous 4 standards (1997–2006) and contains detailed information about automatic identification, data capture techniques, and QR symbol specifications of modern versions [1] It seems that there exists a tacit assumption that problem of QR code detection and recognition is already solved. The detection and recognition of QR code become more challenging, when multiple symbols are present on a given scene
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