## 2 Related Work  
Prior published approaches for recognizing QR Codes in images can be divided into Finder Pattern based location methods [6; 7; 8; 9; 10; 11; 12] and QR Code region based location methods [13; 14; 15; 16; 17; 18]. The first group locates a QR Code based on the location of its typical Finder Patterns that are present in its three corners. The second group locates the area of a QR code in the image based on its irregular checkerboard-like structure (a QR Code consists of many small light and dark squares which alternate irregularly and are relatively close to each other). A shape of the Finder Pattern (Figure 3) was deliberately chosen by the authors of the QR Code, because \"it was the pattern least likely to appear on various business forms and the like\" [6]. They found out that black and white areas that alternate in a 1:1:3:1:1 ratio are the least common on printed materials. In [7] ([PERSON] and [PERSON]) all points matching the 1:1:3:1:1 ratio, horizontally and vertically, are collected. Collected points belonging to one Finder Pattern are merged. Inappropriate points are filtered out according to the angle between three of them. In [8] ([PERSON] et al.) minimal containing region is established analyzing five runs in labeled connected components, which are compacted using run-length coding. Second, coordinates of central Finder Pattern in a QR Code are calculated by using run-length coding utilizing modified Knuth-Morris-Pratt algorithm. In [9] ([PERSON] and [PERSON]) two-stage detection approach is proposed. In the first stage Finder Pattern (located at three corners of a QR Code) is detected using a cascaded classifier trained according to the rapid object detection method (Viola-Jones framework). In the second stage geometrical restrictions among detected components are verified to decide whether subsets of three of them correspond to a QR Code or not. In [10] ([PERSON] and [PERSON]) Finder Pattern candidate localization is based on the cascade of boosted weak classifiers using Haar-like features, while the decision on a Finder Pattern candidate to be kept or dropped is decided by a geometrical constraint on distances and angles with respect to other probable Finder Patterns. In addition to Haar-like features, local binary patterns (LBP), and histogram of oriented gradients (HOG) based classifiers are used and trained to Finder Patterns and whole code areas as well. In [11] ([PERSON] and [PERSON]) successive horizontal and vertical scans are launched to obtain segments whose structure complies with the ratio 1:1:3:1:1. The intersection between horizontal and vertical  
Figure 3: Finder Pattern. Figure 2: Other types of a QR Code: (**a**) Micro QR code, (**b**) iQR code. segments presents the central pixel of the extracted pattern. All the extracted patterns are transmitted to a filtering process based on principal components analysis, which is used as a pattern feature. In [12] ([PERSON] and [PERSON]) seven Hu invariant moments are applied to the Finder Pattern candidates obtained by initial scanning of an image and using Euclidean metrics they are compared with Hu moments of the samples. If the similarity is less than experimentally determined threshold, then the candidate is accepted. In [13] ([PERSON] et al.) authors introduce algorithm, which aims to locate a QR Code area by four corners detection of 2D barcode. They combine the Canny edge detector with external contours finding algorithm. In [14] ([PERSON] and [PERSON]) they use histogram correlation between a reference image of a QR code and an input image divided into a block of size \(30\times 30\). Then candidate blocks are joined into regions and morphological erosion and dilation is applied to remove small regions. In [15] ([PERSON] and [PERSON]) they propose approach which uses Canny edge detection followed by morphological dilation and erosion to connect broken edges in a QR Code into a bigger connected component. They expect that the QR Code is the biggest connected component in the image. In [16; 17] ([PERSON] et al.)