NEW TECHNIQUE FOR SKEW ANGLE DETECTION OF TEXT IN IMAGE DOCUMENT

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1 NEW TECHNIQUE FOR SKEW ANGLE DETECTION OF TEXT IN IMAGE DOCUMENT Khalil Ibrahim Alsaif 1 Montaha Tariq Alsarraj 2 Ass. Prof. Dr. M.sc. Student Dept. of Computer Science College of Computer& Mathematic Science - Mosul Univ./ Iraq khalil_alsaif@hotmail.com ; montahatariq@yahoo.com Abstract Image text document can be acquire via scanner or other type of machines which cause sometime a skew angle (alignment) on its horizontal direction so many techniques were developed to treat that skew. In This Research a new idea based on Fan filter will be achieved to detect the skew angle of a text image. A fan filter will be designed in different level and size to be suitable to decompose the image to its component with the same number of filter level, then looking for which coefficients hold the highest energy. The skew angle of the text image will be extracted from The fan filter component rank which match the highest energy component in the image slices. The proposed algorithm applied on different type of skew with different images. The difference between the measured angle and the actual angle in the range of ± 1.5º. the proposed algorithm was simulated using Matlab software Version (R2011a). Keywords: Image processing, image documents processing, Fan filter, Directional filter, contourlet transformation. 1- INTRODUCTION Papers converted into electronic forms pass via, processing techniques which is usually include three steps (Cao, Shuhua, & Li, 2003). 1. Scanning 2. Digitalization 3. Layout analysis and character recognition. A skew angle on the scanned document is inevitably introduced into the converted document due to various factors Optical character recognition is the process of converting digital image of text, such as a scanned paper document or electronic fax file, into computer-editable text. Typical application of an OCR is to convert a scanned image of text on paper to a text document that can be further processed on a computer In general, there are five major stages in OCR processing (R., Imran, & S., 2011): 1) Preprocessing 2) Segmentation 3) Representation 4) Training and Recognition 5) Post Processing The definition of a skew angle in image document is the angle between the text lines and the horizontal direction of the document image. 102

2 The page orientation of a document is defined as the printing direction of text lines. Therefore the page orientation can be in either horizontal printing mode (portrait mode) or vertical printing mode(landscape mode). The skew angle of a document can be defined also as the orientation angle of text lines (Daniel, George, & Harry, 1994). 2- AIM OF THE PAPER In this paper a new approach for image document skew angle detection is introduced by achieving the idea of convolving a special mode of fan filter bank with different directions ( 8 up to 64 direction ) with the document image to detect its skew angle. 3- RELATED WORKS Changming Sun and Deyi Si 1997 in their research "Skew and Slant Correction for Document Images Using Gradient Direction", description the development and implementation of fast algorithm are presented (Changming & Deyi, 1997). Yue Lu, Chew Lim Tan 2009 in their research "Improved Nearest Neighbor Based Approach to Accurate Document Skew Estimation" The nearest-neighbor based document skew detection methods do not require the presence of a predominant text area, and are not subject to skew angle limitation (Yue & Chew, 2009). Gaofeng M. and et. al in their research "Skew Estimation of Document Images Using Bagging" a general-purpose method for estimating the skew angles of document images, rather than to derive skew angle merely from text lines, the proposed method exploits various types of visual cues of image skew available in local image regions (Gaofeng & et.al., 2010). Alireza Alaei1, Umapada Pal, P. Nagabhushan1 and Fumitaka Kimura 2011 in their research " A Painting Based Technique for Skew Estimation of Scanned Documents" they propose a skew estimation Technique based on Piece-wise Painting Algorithm (PPA) for scanned documents (Alireza & et.al., 2011). Darko Brodić,Dragan R. Milivojević 2012 in their research "An Algorithm for the Estimation of the Initial Text Skew" presents a methodology for the estimation of the initial skew rate of text lines (Darko & Dragan, 2012). Prakash K Aithal, Rajesh G, U Dinesh Acharya and Siddaling aswamy P. C 2013 in their research "A Fast and Novel Skew Estimation Approach using Radon Transform" a skew estimation technique for machine printed documents and photos using radon transform is proposed (Prakash & et.al., 2013). 4- FAN FILTERS BANK A digital filter bank is a set of bandpass filters that have a common input or provide an overall result. The simplest type of filter bank is one consisting of only two filters. In this case, the input signal is decomposed into two subbands by using a lowpass and a highpass filter.these filters are called "analysis" filters. The inverse of this procedure is the synthesis of the two subbands into one signal. The filters used for this procedure are called "synthesis" filters. The bandwidth of each subband is limited in fewer frequencies than the original signal. As a consequence, the sampling rate can be reduced before any further processing. Reducing the sampling rate allows for more efficient processing of the signals. In order to reconstruct the signal, the sampling rate of the subbands has to be increased before applying the synthesis filters (Stamos, 2011). 5- FILTER BANKS In 1992, Bamberger and Smith (M. & M., 2003) introduced a 2-D directional filter bank (DFB)that can be maximally decimated while achieving perfect reconstruction. The DFB is efficiently implemented via a ɭ-level tree-structured decomposition that leads to 2 ɭ subbands with wedge-shaped frequency partition as shown in Figure (1). 103

3 Figure 1. Directional filter bank frequency partitioning where ɭ= 3 and there are 2 3 = 8 real wedge-shaped frequency bands. The original construction of the DFB in (M., 2001) involves modulating the input signal and using diamond-shaped filters. Furthermore, to obtain the desired frequency partition, an involved tree expanding rule has to be followed (S., M., & R., 1999). As a result, the frequency regions for the resulting subbands do not follow a simple ordering as shown in Figure (1) based on the channel indices. In (M. & M., 2003),we propose a new formulation for the DFB that is based only on the QFB's with fan flters Figure (2). The new DFB avoids the modulation of the input image and has a simpler rule for expanding the decomposition tree. Intuitively, the wedge-shaped frequency partition of the DFB is realized by an appropriate combination of directional frequency splitting by the fan QFB's and the "rotation" operations done by resampling, which are illustrated in Figure(2) and Figure(3), respectively. Figure 3. Example of a resampling operation that is used effectively as a rotation operation for the DFB decomposition. (a) The "cameraman" image. (b) The "cameraman" image after being resampled. The following four basic unimodular matrices are used in the DFB in order to provide the equivalence of the rotation operations (M., 2001): Figure 4 : shows an example of a resampled image. Note that R0R1 =R2R3 = I2 ( here I2 denotes the 2 2 identity matrix ) so that,for example, upsampling by R0 is equivalent to downsampling by R1. Figure 2. Two-dimensional spectrum splitting using the quincunx flter banks with fan flters. The black regions represent the ideal frequency supports of each flter. Figure 5: Example of a resampled image. (a) The cameraman image. (b) The cameraman image after being resampled by R0. A useful tool in analyzing multi-dimensional multirate operations is the Smith form that can diagonalize any integer matrix M into a product UDV where U and V are unimodular integer matrices and D is an integer diagonal matrices. The quincunx matrix in (1). 104

4 ..(1) can be expressed in the Smith form as Where...(2)..(3) are two 2-D diagonal matrices that correspond to dyadic sampling in each dimension. For the interchange of filtering and sampling, multirate identities can be used. The identity for the analysis side of the filter bank is shown in Figure(6) ;the one for the synthesis side can be inferred similarly. Downsampling by M followed by filtering with a filter H(ω) is equivalent to filtering with the filter H(MTω), which is obtained by upsampling H(ω) by M, before downsampling. Figure 6: Multi-dimensional multirate identity for interchange of downsampling and filtering. 6- SOME TECHNIQUES FOR SKEW ESTIMATION : There are several methods for skew detection. These methods are based on 1. Hough Transform 2. Analysis Projection Profile 3. nearest neighbor clustering 4. Cross Correlation 5. Other techniques for skew estimation.:- Sauvola and Pietikainen (J. & M., 1995) propose an approach for skew detection based on gradient direction analysis, that may be applied to binary or gray-level images. The image is undersampled and convolved with two masks in order to get the gradient map. (magnitude and direction). The local dominant directions for each cell of a grid is computed using the gradient information. The histogram of such directions, discarding flat subwindows, is computed after an angle quantization. The maximum of the resulting histogram estimates the text direction. A similar technique is used by Sun and Si (Changming & Deyi, 1997). The basic assumption is that in a typical document there are more points whose gradient orientations are perpendicular to the text lines. The histogram of the gradient orientation of the gray-level image is computed. The histogram is then smoothed with a median filter in order to reduce undesired effects due to quantization. The mode of the histogram gives an estimate of the skew. Chen and Haralick (S. & R., 1994).present a text skew estimation algorithm based on opening and closing morphological transforms (S. & R., 1995). The recursive closing transform is computed with a structuring element 2X2 or 2X3, depending on the expected range of the skew angle. The resulting image (that is a sort of anisotropic distance map) is binarized by a global threshold estimated from its histogram. An interesting approach is presented by Aghajan et al. (H., B., & T., 1994). Skew estimation is reformulated as the problem of determining the direction of arrival of planar electromagnetic waves detected by a linear sensor array. At the top of the image columns virtual sensors are placed which measure the signal generated by a set of straight lines in the image plus noise. A spectral analysis of the measurement vector takes place using a subspacebased technique (TLS-ESPRIT algorithm) for array processing. The algorithm potentially is capable to detect multiple skew angles although in the experiments a unique skew direction is assumed. The authors claim that the method is robust to the presence of formulas or diagrams and works well both on binary and gray-level images (Cattoni & et.al., 2009) 7- PROPOSED ALGORITHM The proposed algorithm for skew angle detection can be summarized by the following steps:- 1- An image has to be acquired via a scanner devices then stored as an image file. 2- A smallest paragraph to be extracted from the image using side projection technique, which will be the implemented by the algorithm. 105

5 3- Perform pre processing on the image which include : * Convert the image to a binary image, * Check size and resize it if need. * Convert the text image to a Negative image. 4- Select the filter name :- (Pfilter = maxflat Dfilter = dmaxflat7). 5- determine the level of the Directional filter within the following levels (4,8,16,32,64) or according to the required accuracy. 6- Build the directional filter according to the given level(n) which will specify rotated angle (Theta = 180/N) 7- Apply mathematical convolution between the text document and each slice of the directional filter(i.e. for each angle), which is a decomposing process based on filter angles. 8- Calculate energy (Energy) for each slice image : Then select the image which has the highest value of energy (image sequence cc). 9- Calculate the skew angle by equation Tha=180/N*(cc-1) 8- RESULTS OF APPLIED EXAMPLE Proposed Algorithm was Applied on a Single Paragraph of The English Text (Font Type :Times New Roman, Font Size: 14) Consists text of one line has Slant on the horizontal at an angle (45). The text treated by level( 4 ) filter and figures(7-11) view the results of each step of the proposed algorithm. ( a) (b) Figure(7):represent (a) original Image skewed by 45 0 (b) Negative for original Image. [ ] ( ) (a) Filter Image Plots (b) Filter Line Plots (C) Filter 3D surfaces Figure (8): Fan Filter With Angle 0 Degree 106

6 (a) Filter Image Plots (b) Filter Line Plots (C) Filter 3D surfaces Figure (9): Fan Filter With Angle 45 Degree (a) Filter Image Plots (b) Filter Line Plots (C) Filter 3D surfaces Figure (10): Fan Filter With Angle 90 Degree (a) Filter Image Plots (b) Filter Line Plots (C) Filter 3D surfaces Figure (11): Fan Filter With Angle 135 Degree 107

7 Convolution Process was done on whole coefficients of the Filter and image document, then the energy for each output slide was measured and angle of rotation which produced highest energy will represent The Skew Angle Of The Text Image.figures (12)&(13) show the algorithm sensitivity on the filter level, and the fount size, which are clearly seen in figures Real Angle When N=4 When N=8 Estimated Angle When N=16 When N=32 When N= Figure (12): Represent Relationship Between Skew Angle and Filter Level Real Angle Estimated Angle When font size=8 When font size=18 When font size= Figure (13): Represent Relationship Between Skew Angle and Font Size 108

8 9- DISCUSSION from experiments were conducted to test the algorithm and accuracy to Estimate the skew angle of image of text that has different font sizes and different skew angles. The proposed algorithm which has been applied to most of the skew cases Possible on the text in font size and different angles it be linear relationship between the real angle and the angle at which it was measured with a small error rate of not more than (0.1). In the worst possibilities when the font size under (40) and increased the ratio to become (0.2), when the font size (40), and this proves stability of the proposed algorithm in estimate skew angle in the text image consisting of a font size normally not more than (25). When testing the proposed algorithm changes the level of filter at the same real skew Angle of text image and different levels of the filter. It seems clear that the level of filter vector has an effect on algorithm performance and accuracy and thus the resulting value of the skew angle. Experimental application shows that high value of filter give that measured angle is closer to real skew angle. The algorithm has been applied on text image scanned in different resolution The results showed that the algorithm is stable when values of resolution between ( 100 = < DPI < 300). When applied the algorithm on the hand-written text, the performance has been very close to its performance in the case of printed text. 10- CONCLUSION In this paper, a novel skew detection approach is firstly introduced. The performance of the proposed algorithm was measured on (6) testing document images obtained by scanning seven different document types including 1- Same real skew angle(30) for different Font Size, 2-Same real skew angle(60) for different Font Size, 3- Same Fonts Size with different real skew angle, 4-different directional Filter level, 5-Text image scanned in different Resolution, 6- Image for handwritten documents. The algorithm can detect large skew angles in a document, and tests show that the method is accurate for angles of up to REFERENCES 1. Alireza, A., & et.al. (2011). " A Painting Based Technique for Skew Estimation of Scanned Documents ". International Conference on Document Analysis and Recognition, (pp ). Beijing, China. 2. Cao, Y., Shuhua, W., & Li, H. (2003). " Skew Detection and Correction in Document Images Based on Straight-Line Fitting ". Pattern Recognition Letters, 24 (12), Cattoni, R., & et.al. (2009). " Geometric Layout Analysis Techniques for Document Image Understanding ". ITC-IRST Italy Technical Report. 4. Changming, S., & Deyi, S. (1997). " Skew and Slant Correction for Document Images Using Gradient Direction ". ICDAR 97 Proceedings of the 4th International Conference on Document Analysis and Recognition, 1, pp Daniel, X. L., George, R. T., & Harry, W. (1994). " Automated Page Orientation and Skew Angle Detection for Binary Document Images ". Pattern Recognition, 27 (10), Darko, B., & Dragan, R. M. (2012). " An Algorithm for the Estimation of the Initial Text Skew ". Information Technology and Control, 41 (3), Gaofeng, M., & et.al. (2010). " Skew Estimation of Document Images Using Bagging ". IEEE Transactions on Image Processing, 19 (7), H., K. A., B., H. K., & T., K. (1994). " Estimation of Skew Angle in Text-Image Analysis by SLIDE: Subspace-based Line Detection ". Machine Vision and Applications, 7, J., S., & M., P. (1995). " Skew Angle Detection Using Texture Direction Analysis ". In Proc. of the 9th Scandinavian Conference on Image Analysis, (pp ). Sweden. 10. M., N. D. (2001). " Directional Multiresolution Image Representations ". Switzerland: Ph.D. Dissertation, Department of Communication systems, Swiss Federal Institute of Technology Lausanne,. 11. M., N. D., & M., V. (2003). " Contourlets ". In J. Stoeckler and G. V. Welland, editors, Beyond 109

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