SKEW DETECTION AND CORRECTION

Transcription

1 CHAPTER 3 SKEW DETECTION AND CORRECTION When the documents are scanned through high speed scanners, some amount of tilt is unavoidable either due to manual feed or auto feed. The tilt angle induced during scanning process is called Skew angle. In case of handwritten documents the skew angle could be global due to wrongly placed document or local skew due to the inclined writing by the user. The presence of skew angle can adversely affect the overall process of recognition and significantly complicate the segmentation of document. Hence, it is required to detect and correct the skew angle before proceeding to further steps in OCR. To calculate approximately the skew angle of a text line, an imaginary line can be drawn through its characters. The angle of this straight line with the horizontal edges of the page is the skew angle of the text line as shown in the Figure 3.1. Figure 3.1: A Skewed Text Line * Some parts of the material in this chapter appeared in the following research paper 1. S Prabhanjan, Dinesh R, Santosh Naik A Novel Method for Document Skew Detection and Correction : Application to, Handwritten Document and Bank Documents, International Journal of Applied Engineering Research ISSN Volume 10, Number 14 (2015) [scopus Indexed] 44

2 Skew correction mechanism Once the skew angle is identified, the document is simply rotated in the opposite direction by the estimated angle. Hence there is a need for a trustworthy technique to detect and correct skew in the scanned document. In this chapter, we presented a novel method for skew angle estimation and correction. The proposed approach is based on morphological operation and linear regression process. In subsequent sections details of the proposed skew detection and correction algorithm presented. The efficiency of the method has been established by applying the skew detection and correction algorithms on unconstrained, constrained, printed Devanagari script and bank forms. The result presented in the table for proposed algorithm is encouraging. 3.1 Proposed Method The proposed algorithm relies on detecting and correcting the skew angle of a document using connected component analysis of the binarized text lines. We study the shape structure of the connected components using the morphological operations and finally, for each shape structure we fit a line using the linear regression to determine the skew angle of the document. Once, the skew angle is estimated, skew in the document is corrected by rotating the entire document in opposite direction. This skew detection and correction method can be applied to unconstrained, constrained, printed Devanagari script, skewed words and scanned bank forms. The proposed method performs well compared to many of the available techniques because it can correct texts containing images, diagrams, etc. The proposed method for skew detection and correction system is shown in the Figure Binarization Colored or grayscale image converted to bi-level information using Otsu'[144] global thresholding. Logical and semantic content of the document should be understood during thresholding. Documents used in our experiments were even colored paper, hence global thresholding method considered for binarizaion as shown in Figure

3 3.1.2 Finding the connected Component having longest Width To find size of the structuring element, width w of the connected component having longest width in the document found using connected component labeling is used as width of structuring element and length of structuring as 4 as shown in Figure Morphological Close Operation To connect all words in a line, morphological close operation performed using the structuring element found in step 2 as shown Figure Thinning Smear the text lines to lines of white bands using a line structuring element (SE) of length l pixel, say using morphological closing operations. Width of structuring element varies depending on the font size and scanner resolution. Output of the thinning process upon elimination of contours of non-text regions are shown in Figure Find connected components Each line the text in the document represented as single connected component. Find all these connected components using connected component labeling [142] algorithm Fit the lines to connected components To determine the skew angle, a line is fitted to each of the connected component found in previous step using polynomial line regression as shown in Figure Skew angle estimation To estimate the skew angle, average the slope of each fitted line gives the estimated skew of the document Skew Angle Correction α is the estimated skew angle found in previous step, to correct the skew document is rotated in opposite direction of estimated skew angle by - α. 46

4 Figure 3.2: Proposed method 47

5 (a) (b) Figure 3.3: (a) original Image (b) Binarized Image 48

6 Figure 3.4: Connected components in the document Figure 3.5: Connected components are connected using closing operation 49

7 Figure 3.6: Thinned Image Figure 3.7: Line fitted to connected Component using polynomial line regression 50

8 3.2 Experimental Results and Discussions Figures below shows some selected examples showing the skewed document image (top) and the skew corrected document image using our proposed method (below). We have experimented the proposed method for 200 documents consisting of printed, unconstrained, constrained Devanagari documents and bank documents. Handwritten Devanagari script as shown in Figure 3.8. The skewness of the document image is corrected correctly. (a) (b) Figure 3.8: (a) Skew document (b) Skew corrected handwritten Devanagari document. 51

9 Document having Skewed Handwritten Devanagari word as shown in Figure 3.9. Word is de-skewed correctly. (a) (b) Figure 3.9: (a) Skewed handwrittenword (b) Skew corrected handwritten Devanagari word A bank application form as shown in Figure Application form de-skewed correctly. 52

10 (a) 53

11 (b) Figure 3.10: (a) Skewed bank from (b) Skew corrected bank application form 54

12 A printed Devanagari Script as shown in Figure The document is skewed correctly. (a) (b) Figure 3.11: (a) Skewed printed document (b) skew corrected Devanagari printed document To establish the superiority of the proposed method we have compared the proposed method with existing contemporary algorithm. Table 3.1 presents the result obtained by our proposed method and compared results with the other existing methods. The aim is to obtain a 100% success rate as fast as possible. It is clear from Table 3.1, that the proposed method is better 55

13 compared to other existing methods in the success rate. Average time taken by our algorithm for skew detection and skew correction is approximately 2.11s. Table 3.2 shows the true angle and corrected angle by proposed method. Table 3.1: Shows the comparison of proposed method with the existing methods. Proposed By Document Type Angle Method Baird[115] Printed Document ±15 Projection Profile Srihari[116],Pal[117] Printed Document ±45 Hough Transformation Postal[141] Printed document ±45 Fourier Transformation B.J.Kshirsagar[143] Printed, Handwritten ±25 Rotation and Correction Method Tian Jipeng[121] Handwritten characters, numerals ±90 Hough Transformation Proposed Method Handwritten and printed Devanagari script and words, Bank Forms ±45 Morphological,linear regression and connected component labelling [Table 3.2: Shows the true angle and corrected angle of the proposed method. True Angle in Degree Proposed method Corrected angle

14 Angle Limitation: The proposed algorithm corrects skews up to an angle ±45. For a large skewed document, proposed method may create a bridge between text lines leading to the failure of the algorithm. However, the common skew angle is limited to ±5 for all practical purposes; proposed method result is well within the operational limit. If the document is skewed at 225, our algorithm detects and corrects the skew but not its orientation (the document will be 180 flipped). Our algorithm is content independent. The skew of a text with graphs, tables, diagrams, etc. can be corrected. Accuracy: The proposed algorithm corrected skews of our tested documents with accuracy of 0.06 degrees. There is no need to correct for lesser angles as angles less than 0.1 degree have minimal or no effect on performance of character recognition algorithms. 3.3 Conclusion In this chapter, we presented a novel method for detection and correction of skew present in scanned documents. Our technique is based on structure analysis using morphological operation and line fitting using linear regression, which works for unconstrained, constrained, printed Devanagari script and bank forms with angle limitations (up to ±45 ). A comparison of proposed method with the existing skew angle methods proved a reliable and outperforming the compared methods. However, correcting the up-side down document is beyond the scope of this work. 57