# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
## 2 Related Work  
De-warping approaches for document images have received much attention in the scientific community in recent years, and various approaches have been presented. Three types of approaches are available: text-based, shape-based, and deep-learning-based. The first category depends on extracting features from text lines, words, and characters. The ridge-based coupled-snakes model was employed among the methods described in [4; 5]. These authors used the coupled-snakes model to calculate the baseline, then determined the starting location of each text line. Finally, the perspective distortion was removed using a four-point homography technique. [PERSON] et al. [6] developed a technique for introducing a vector field from the generated warping mesh based on local orientation data. The image is fixed by using numerous linear projections to approximate the nonlinear distortion. By using a baseline detector, they were able to extract the needed local orientation characteristics. [PERSON] et al. [7] introduced a method based on discrete representations of text blocks and text lines, which are sets of connected components. Each text line's features were retrieved in this study. Using the Levenberg-Marquardt technique, a cost function was created to  
Figure 1: Example of document-image warping. solve the warping. To overcome the de-warping problem, a few approaches based on word information have been presented. [PERSON] et al. [8] have suggested breaking the document into words. The orientation and orientation of each word are determined. Finally, to correct the problem of warping, each word is rotated. A curled baseline pair was utilized by [PERSON] et al. [4]. Each corresponding top and bottom baseline pair's map characters are computed. The restoration procedure was carried out by [PERSON] et al. [9] using segmentation and thin plate splines. The text baselines and vertical stroke boundaries for each line were determined by [PERSON] et al. [10]. [PERSON] et al. [11] presented a method that covered each letter with a quadrilateral cell and then computed the center of each letter to adjust its orientation, resulting in a flat word. The most common methods are the text-information based. They work well if the segmentation method is correct. On the other hand, these systems have the disadvantage of being susceptible to high degrees of curl and varying of line spacing. They are also sensitive to the image resolution and words used. Text-based algorithms are time-consuming and fail when dealing with complicated layouts. They also failed when it comes to documents with graphics and tables in them. These approaches are susceptible to a variety of distortions and result in a high number of segmentation mistakes. The second category is shape recovery. These methods are based on recovering the shape of document. As shown in [12], these techniques rely on 2D-form recovery employing a curved surface-projection map to restore a rectangular 2D region. The uniform parameterization procedure in [13] is guided by a physical pattern. The authors of [14] use a technique that separates the page into three vertical sections and projects each one separately. The approaches presented in [15,16] use text lines to establish the curved shape, then use a surrounding transformation model to flatten it. The concept behind 3D reconstruction is to turn a coiled paper into three dimensions (x, y, and z). De-warping distortion may be addressed by first determining the depth (z) dimension and then fitting the curved form [17, 18, 19, 20]. Laser range scans [21], stereo cameras, eye scanners [22], controlled lighting [23], and computed tomography (CT) scans for opaque objects [18,24,25] are examples of specialized equipment needed in this procedure. In order to acquire the 3D form of the coiled document, previous knowledge is also ecassary. The majority of 3D methods do not rely on the document's content. As a result, these approaches are more precise in representing the shape than 2D methods.