# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
## 4 Results  
Our method was evaluated on a workstation Core i5 2.5 GHz CPU and implemented in MATLAB R2020a. The performance of our algorithm was experimentally tested on some samples of warped document images. These samples have different levels of complexity, including, for example, degradation, handwriting, different languages, and different layouts. Figure 10 illustrates the result of the document image before and after warping correction. Figure 10 depicts the original document image, which it has numerous degradations, challenges, warping, and handwritten text. The document image after solving the degradation and warping problems is illustrated in Figure 10. The corresponding checkerboard calibration pattern which was used is introduced in Figure 10. Figure 10 presents the checkerboard after the de-warping process. We used two different types of testing to analyze and validate the performance of our results: quantitative analysis based on OCR engines (optical character recognition) (error rate Er) and human visual perception. Figures 10-12 show that our proposed method is more readable and easier to comprehend based on visual criteria. Figure 11: Recent de-warping method comparison. (**a**) warped image (**b**) de-warped image by [36]. (**c**) de warping image by [37] (**d**) proposed de-warped image. Figure 10: De-warping of the image. (**a**) original image. (**b**) de-warped image. (**c**) corresponding checkerboard of the warped document. (**d**) de-warped checkerboard. Quantitative analysis based on the OCR engine is used to evaluate and validate the performance of our results, and to test the text region of the proposed results against the state-of-the-art methods. In this work, the ABBYY Fine Reader OCR program was used. The ABBYY program is used to compute the number of characters in the document image before and after the application of a de-warping technique. The software is used to convert image files, scanned documents, and PDF documents into editable formats. The ABBYY application is employed to calculate the number of characters in the document image. The proposed results are compared to the state-of-the-art methods in [13; 36] using OCR performance as a quantitative analysis. The most popular method for evaluation is to  
Figure 12: The comparison between our approach and other state-of-the-art methods using CBDAR dataset. (**a**) Original document images in CBDAR dataset. (**b**) Method proposed in [14]. (**c**) Method proposed in [4]. (**d**) Method proposed in [13]. (**e**) Our proposed method. use OCR performance (error rate Er). The OCR error rate (Er) metric is the most common method used for evaluation, and works by computing the ratio incorrect characters to the total number of characters in the document image as shown in Equation (12). \[\text{Er}=\frac{incorrect\ character}{all\ character}*100\% \tag{12}\]  
Characters which the OCR fails to recognize are called incorrect characters. Table 1 illustrates the error rate (Er) of our method and the methods in [13; 36]. The proposed method achieved a low error rate. The only available document de-warping dataset (CBDAR 2007) was employed to evaluate our method. A total of 102 camera-captured documents are included in this dataset, along with their associated ASCII ground-truths. We compared our method with other state-of-the-art approaches in [4; 13; 14]. Figure 12 presents two examples from the dataset. The selected two images have one of the most challenges that most of the previous methods fail to solve. Figure 12a is the original warped document image from dataset. Figure 12b shows the de-warped image proposed by [14]. The method in [14] produced bad results in the equation portion of the left image and failed when the right document image contained figures. Figure 12c shows the de-warped image proposed by [4]. The results of the method in [4] are better than the method in [13] in the left image but still failed in the right image due to the figure inside the document.