A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images

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1 A Laplacian Based Novel Approach to Efficient Text Localization in Grayscale Images Karthik Ram K.V & Mahantesh K Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore, India. karthikramkv, gmail.com Abstract - Text detection in video images has received increasing attention, particularly in scene text detection in video images, as it plays a vital role in video indexing and information retrieval. In this paper, we propose an efficient text localization method based on the Laplacian operator. The maximum gradient difference value is computed for each pixel in the Laplacian-filtered image. Thresholding is then used to classify all the pixels into two groups: text and non-text. Connected Component Analysis and Morphological operations are then applied to eliminate the false positives based on geometrical properties. Experimental results show that the proposed method is able to detect text of different fonts, contrast and backgrounds. Moreover, it outperforms three existing methods in terms of better detection and lower misdetection rates. Index Terms Laplacian Operator, Maximum Gradient Difference, Thresholding, Morphological Operators I. INTRODUCTION There is an increasing number of video databases on the Internet and a reliable source of information for searching and retrieval is the text that appears in the videos. Video text consists of two types: graphic text and scene text. Graphic text is artificially added to the video during the editing process. Scene text appears naturally in the scenes captured by the camera. Although many methods have been proposed over the past years, text detection is still a challenging problem because videos often have low resolution and complex backgrounds and text can be of different sizes, styles and alignments. In addition, scene text is usually affected by lighting conditions and perspective distortions [1 3]. Text detection methods can be classified into three approaches: connected component-based [4], edge based [5 9] and texture-based [10 14]. The first approach does not work well for all video images because it assumes that text pixels in the same region have similar colours or grayscale intensities. The second approach requires text to have a reasonably high contrast to the background in order to detect the edges. So these methods often encounter problems with complex backgrounds and produce many false positives. Finally, the third approach considers text as a special texture and thus, uses fast Fourier transform, discrete cosine transform, wavelet decomposition and Gabor filters for feature extraction. However, these methods require extensive training and are computationally expensive for large databases. In this paper, we consider three existing methods [7, 8, 15] for comparative study. Liu et al. [7] extract edge features by using the Sobel operator. This method is able to determine the accurate boundary of each text block. However, it is sensitive to the threshold values for edge detection. Wong et al. [8] compute the maximum gradient difference values to identify candidate text regions. This method has a low false positive rate but uses many threshold values and heuristic rules. Therefore, it may only work well for specific datasets. Finally, Mariano et al. [15] perform clustering in the L*a*b* colour space to locate uniform coloured text. Although it is good at detecting low contrast text and scene text, this method is extremely slow and produces many false positives. We propose a text detection method which consists of three steps. Dataset Collection, Feature Extraction using Laplacian Operator and Maximum Gradient Difference and Text Localization process and later it is compared with the existing well known methods as witnessed in [7, 8 & 15]. The rest of the paper is organized as: The proposed Methodology is explained in section II. Experimental Results and Performance Evaluations are discussed in section III and finally Conclusion is given in section IV. 126

2 II. PROPOSED METHODOLOGY In this section we are mainly concentrating on three different phases such as Dataset Collection, Feature Extraction using Laplacian Operator and Maximum Gradient Difference, Text Localization process. The detailed explanation is given in following subsections: A. Dataset Collection For our work, we are using the images taken from ICDAR-2003 database. The database consists of images of the cover pages of the text books, emergency sign boards, bus names, number plates [16] and available in [17]. B. Feature Extraction using Laplacian Operator and Maximum Gradient Difference We propose a two-step novel approach to feature extraction using Laplacian Operator and Maximum Gradient Difference. Text regions typically have a large number of discontinuities, e.g. transitions between text and background. Therefore, the input image is converted to grayscale and filtered by a 3 3 Laplacian mask shown in figure 1 [18], to detect the discontinuities in four directions: horizontal, vertical, up-left and up-right. Fig1. 3 x 3 Laplacian Mask Because the mask produces two values for every edge, the Laplacian-filtered image contains both positive and negative values. The transitions between these values (the zero crossings) correspond to the transitions between text and background. In order to capture the relationship between positive and negative values, we use the maximum gradient difference (MGD), defined as the difference between the maximum and minimum values within a local 1 N window [8]. The MGD value at pixel (i, j) is computed from the Laplacian-filtered image f as follows. MGD(i, j) = max( f (i, j t)) min( f (i, j t)) (1) Where t (n 1) 2, (n 1) 2 (2) The MGD map is obtained by moving the window over the image. Text regions typically have larger MGD values than non-text regions because they have many positive and negative peaks. C. Text Localization Process Text Localization and Recognition is performed on features extracted from previous section in two levels such as, Thresholding, Connected Component Analysis and Morphological Operations. Thresholding: Thresholding is a simple technique for image segmentation. In this subsection, we employ dynamic thresholding [13] to calculate the global threshold value T. A global threshold (T) is determined based on the average value of gradient difference computed as follows. First we compute the average gradient values as: AVG = 1 (n x m) m n j=1 (3) i=1 MGD(i, j) where n,m are the dimension of the maximum gradient image. Next we count the number of High Gradient Values as: NHG = count (( G(i,j) > AVG )) The sum of MGD is computed as: (4) m n SGD = i=1 j=1 MGD(i, j) (5) Finally the value of global threshold T is computed as: T = (SGD) ( n x m NHG ) (6) Then a pixel is classified based on global threshold T as follows: pixel = Text Pixel, if (MGD i, j > T) Non Text Pixel, if(mgd i, j < T (7) 2) Connected Component Analysis and Morphological Operations: After thresholding, still some non-text regions exists which are removed by applying connected component analysis and morphological operations opening and dilation. For the image obtained after applying threshold morphological opening operation is applied by taking 75 pixels as area. So the blocks whose area less than 75 are removed. After the opening, some discontinuities will be there text regions, which can be removed by morphological dilation. For the resultant image, morphological dilation operation is applied by taking 5x5 matrix of 1s as the structural element. Finally the bounding boxes are drawn for the blocks remaining after all these steps. Almost all blocks will be the text regions, still some more non-text blocks will be present, to those also bounding boxes are drawn. III. EXPERIMENTAL RESULTS AND PERFORMANCE EVALUATION The entire work has been carried out using the software Matlab (R2010a) installed on a system having Intel Pentium 4 processor which is having 2.73 GHz as clock speed and 1.24 GB of RAM. 127

3 Experimental Results and Performance Evaluation are discussed in the following sub-sections. A. Experimental Results For our work, we are using the images taken from ICDAR 2003 database. The database consists of images of the cover pages of the text books, emergency sign boards, bus name number plates[16]. The database has been downloaded from [17]. Since they are colored, we convert them into gray-level before applying the proposed algorithm. Figure 2(a-c) shows the original image, laplacian filtered image and image obtained after applying maximum gradient difference. Figure 3(a-b) shows the images obtained after applying threshold and Morphological operations respectively. Figure 4 shows the original image with bounding boxes for the detected blocks. Figures 5,6,7,8 shows the two sample images taken from the database and the outcome of the proposed algorithm for the two sample images respectively. Fig. 4. Text Localized Image (a) (b) Fig. 5. Sample Image 1 (c) Fig. 2. (a) Original Image (b) Laplacian Filtered Image (c) Maximum Gradient Difference Image (a) (b) Fig. 3. (a) After Thresholding (b) After CCA and Morphological Operations Fig. 6. Text Localized Image for Sample Image 1 128

4 Fig. 7. Sample Image 2 Fig. 8. Text Localized Image for Sample Image 2 B. Performance Evaluation Generally the performance is evaluated at the block level, which is a common granularity level in the literature rather than the word or character level. The following terms are defined for each detected block by a text detection method [18]: Truly Detected Block (TDB): A detected block that contains at least one true character. Thus, a TDB may or may not fully enclose a text line. Falsely Detected Block (FDB): A detected block that does not contain text. Text Block with Missing Data (MDB): A detected block that misses more than 20% of the characters of a text line. To evaluate the performance, we manually count the text blocks in the Original Image and is called Actual Text Block (ATB), which is the ground truth for the image. The performance measures are defined as follows [18]: Detection rate (DR) = TDB / ATB False Positive Rate (FPR) = FDB / (TDB + FDB) Misdetection Rate (MDR) = MDB / TDB The performance of the existing methods in comparison with the proposed method for ICDAR-2003 dataset is given in Table I. TABLE - I Performance evaluation of existing and proposed method for ICDAR-2003 dataset Method DR(%) FPR (%) MDR (%) Edge based [7] Gradient based [8] Uniform colored [15] Proposed The performance evaluation of the proposed method for ICDAR-2003 dataset is done on a subset of 15 complex images that we have chosen from the database. By looking at Table I, we can say that the proposed method outperforms the three existing methods in Detection Rate (DR) and Misdetection Rate (MDR). However the False Positive Rate (FPR) is higher for the proposed method which can be compensated by the higher DR and lower MDR than that of the existing methods. The DR of the proposed method is % and is higher than that of the existing methods. Similarly the MDR of the proposed method is 5.95% which is lower than that of the existing methods. Therefore, the proposed method has achieved better detection results than the three existing methods. IV. CONCLUSION In this paper, we propose a laplacian operator and maximum gradient difference based text localization technique which locates both graphic text and scene text with different fonts, size, scripts, contrast and backgrounds. Experimental results and performance evaluation have shown that the proposed method gives a very good detection rate, lower misdetection rate in comparison with the results of three existing methods. 129

5 In the future, we plan to extend this method to text of arbitrary orientation. In addition to this, we are also planning to localize the text in colour images. For colour images, we are planning to use DWT for feature extraction. Before applying DWT, the colour image is decomposed into its R,G and B planes and applying DWT to these individual plane images can yield results with better efficiency. V. REFERENCES [1] J. Zang and R. Kasturi, Extraction of Text Objects in Video Documents: Recent Progress, The Eighth IAPR Workshop on Document Analysis Systems (DAS2008), Nara, Japan, September 2008, pp [2] J. Zhang, D. Goldgof and R. Kasturi, A New Edge-Based Text Verification Approach for Video, ICPR, December 2008, pp 1-4. [3] K. Jung, K.I. Kim and A.K. Jain, Text information extraction in images and video: a survey, Pattern Recognition, 37, 2004, pp [4] A.K. Jain and B. Yu, Automatic Text Location in Images and Video Frames, Pattern Recognition, Vol. 31(12), 1998, pp [5] M. Anthimopoulos, B. Gatos and I. Pratikakis, A Hybrid System for Text Detection in Video Frames, The Eighth IAPR Workshop on Document Analysis Systems (DAS2008), Nara, Japan, September 2008, pp [6] M. R. Lyu, J. Song and M. Cai, A Comprehensive Method for Multilingual Video Text Detection, Localization, and Extraction, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 15, No. 2, February 2005, pp [7] C. Liu, C. Wang and R. Dai, Text Detection in Images Based on Unsupervised Classification of Edge-based Features, ICDAR 2005, pp [8] E. K. Wong and M. Chen, A new robust algorithm for video text extraction, Pattern Recognition 36, 2003, pp [9] P. Shivakumara, W. Huang and C. L. Tan, An Efficient Edge based Technique for Text Detection in Video Frames, The Eighth IAPR Workshop on Document Analysis Systems (DAS2008), Nara, Japan, September 2008, pp [10] Y. Zhong, H. Zhang and A.K. Jain, Automatic Caption Localization in Compressed Video, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 4, 2000, pp [11] K. L Kim, K. Jung and J. H. Kim, Texture- Based Approach for Text Detection in Images using Support Vector Machines and Continuous Adaptive Mean Shift Algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, December 2003, pp [12] Q. Ye, Q. Huang, W. Gao and D. Zhao, Fast and robust text detection in images and video frames, Image and Vision Computing 23, 2005, pp [13] H. Li, D. Doermann and O. Kia, Automatic Text Detection and Tracking in Digital Video, IEEE Transactions on Image Processing, Vol. 9, No. 1, January 2000, pp [14] W. Mao, F. Chung, K. K. M. Lam and W. Siu, Hybrid Chinese/English Text Detection in Images and Video Frames, ICPR, Volume 3, 2002, pp [15] V. Y. Mariano and R. Kasturi, Locating Uniform-Colored Text in Video Frames, 15th ICPR, Volume 4, 2000, pp [16] 2011 International Conference on Document Analysis and Recognition, ICDAR 2011, Beijing, China, September 18-21, IEEE 2011, ISBN [17] est-textloc.zip [18] Trung Quy Phan, Palaiahnakote Shivakumara and Chew Lim Tan, A Laplacian Method for Video Text Detection, th International Conference on Document Analysis and Recognition, pg no

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