# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 2 Related Work  
In the field of DM code image recognition, many researchers have proposed their own methods. These methods generally focus on the preprocessing, detection and positioning, and correction and recognition of DM code images. Many methods improve and optimize the algorithms for detecting, positioning, and correcting DM codes. [PERSON] et al. proposed an efficient algorithm for locating DM codes in images [7]. By using an improved local threshold segmentation technique and a connected component labeling method [32], image regions that may contain data matrix codes are identified; by analyzing the differences in adjacent projections around the finder pattern, the boundaries of the DM code are determined, thereby achieving the location of the DM code in the image. [PERSON] et al. proposed a novel laser-marked DM symbol reading method based on deep learning [33]. By training a convolutional neural network (CNN) model to learn the colors of two adjacent modules and label them depending on whether they have the same color or not, an edge image is generated based on the Kuhn-Munkres (KM) algorithm using the label matrix of DM code images. Finally, DM code images are reconstructed and decoded. [PERSON] et al. proposed a DM code recognition technology based on corner detection and cluster analysis [20]. Firstly, noise and complex background interference are eliminated through image preprocessing; then, the initial positioning of two-dimensional barcode images is obtained through a method based on morphology. Finally, precise positioning is achieved based on corner detection and cluster analysis. [PERSON] et al. proposed an improved precise positioning method for DM codes [18], including four parts: fast positioning and priority sorting of DM code candidate regions based on corner point distribution, extraction of outer contours of DM code candidates, initial determination of the L side by using the improved Hough transform, and precise positioning of the L side by using iterative weighted least squares line fitting. Among them, the traditional Hough transform method needs to traverse every edge point in the image domain, and its computational efficiency is low; by improving the Hough transform based on trigonometric relationships and the characteristics of lines in polar coordinates, computational efficiency can be improved without reducing statistical points. [PERSON] et al. aimed to combat the difficulty of locating DM codes under pollution and multiple perspectives, and proposed a precise positioning method for DM codes under pollution and multiple perspectives [34]. This method establishes a distribution model of internal edges of DM codes in the Hough domain under multiple perspectives and extracts features. The boundary combination corresponding to the maximum feature value is used as the final positioning result. The experiments show that this method has higher robustness for precise positioning of DM codes with scratches, wear, corrosion, geometric distortion, and other problems. [PERSON] et al. applied the concept of the convex hull in computational geometry to barcode positioning and proposed a DM code positioning method based on the convex hull algorithm [35]. The convex hull algorithm overcomes the disadvantage of traditional line detection algorithms based on the Hough transform which require a large amount of storage space due to global detection. The experimental results show that this algorithm can effectively locate distorted or tilted DM code areas. [PERSON] et al. extracted detection patterns according to the characteristics of detection boundaries in the Random transform domain of two-dimensional barcode images [36] and verified through experiments that they can locate DM codes when the minimum module width of DM code images is only 2.7 pixels. [PERSON] et al.