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International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012 Ancient Tamil Script Recognition from Stone Inscriptions Using Slant Removal Method S. RajaKumar, Dr. V. Subbiah Bharathi effect on the recognition accuracy if non-uniform slant correction is applied to the training and testing images, respectively. The remaining part of the paper is organised as follows. Next, the normalisation steps applied to the images of ancient Tamil text including the non-uniform slant correction are described. Section 3 presents the ancient text line recognition system. Experiments and results are discussed in Sect. 4 and conclusions are drawn in the last section of the paper. Abstract— In this paper we apply a novel non uniform slant correction preprocessing technique to improve the recognition of offline ancient Tamil text lines. The local slant correction is expressed as a global optimization problem of the sequence of local slant angles. This is different to conventional slant removal techniques that rely on the average slant angle. In conventional slant correction techniques the average slant angle is estimated and uniformly corrected by a shear transformation. This angle can be estimated by averaging the angles of near vertical strokes, by analyzing projection histograms, or by statistics of chain-coded stroke contours. These techniques perform well under the assumption that the text line is return with a constant slant. However the slant angle fluctuates not only between different words, but in some cases at every character in a word. Keywords—Ancient Tamil Stone Extraction, Slant Removal, Normalization. Inscriptions, II. CHARACTER NORMALIZATION To reduce the impact of different writing styles, a stone inscription text line image is normalized. In the recognition system used for the experiments described in this paper, the normalisation steps described in [11] are applied. Then, the images are transformed using the non-uniform slant correction introduced in [14]. In the first phase, the images of stone inscription text lines are normalised with respect to skew, slant, and baseline position. The skew angle, the slant angle and the position of the upper and the lower baseline are determined globally. The skew correction aligns a line of text with the x-axis of the image and corrects rotations that may have occurred during the writing. The goal of the slant correction is to bring the writing in an upright position. For each line the angle to the vertical axis is corrected. The baseline position correction is an operation based on vertical translation and scaling. The writing is transformed into a standard position and the height is normalised. We refer to [11] for more details about these normalisation steps. In the second phase the non-uniform slant correction is applied. Assume an M(width) x N(height) text line image. The problem of the non-uniform slant correction is equivalent to the problem of estimating the local slant angle at each horizontal position i ϵ [1, M]. In order to represent the local slant angle at i, consider a line segment from (i, 1) to (p i , N), where p i denote the horizontal position of the upper-end of the line segment. Hereafter, this line segment is called the ith correction line. Note that the lower-end of the ith correction line is fixed at (i, 1) while the upper-end is movable horizontally by controlling the variable p i . Figure 1 shows several correction lines. The slope of the ith correction line (i.e., tan −1( pi − i ) / N ) expresses the local slant at position i. Feature I. INTRODUCTION A NCIENT Tamil character recognition from stone inscription is a challenging problem in pattern recognition area. The difficulty is mainly caused by the large variations of individual writing style. Robust feature extraction is very important to improve the performance of Ancient Tamil character recognition system. This paper concentrates on the problem of different writing styles it is usually addressed by normalizing the input images before they are fed in to the recognizer specifically we are interested in writing style that show a non-uniform slant. These types of characters have been handling rather poorly by previous recognition system. In conventional slant correction techniques the average slant angle is estimated and uniformly corrected by a shear transformation. The contribution of this paper is twofold. First, the non-uniform slant correction is applied to entire text lines for the first time. Second, we investigate the impact of the novel slant correction technique on the recognition performance by conducting experiments with a state-of-the-art ancient Tamil text line recognition system. We examine the S.Rajakumar is a research scholar at Sathyabama University, Chennai, India phone:+91-7299305528; e-mail:[email protected]. Dr.V.Subbiah Bharathi was with Eswari Engineering College, Chennai. He is now as the Principal of DMI College of Engineering, Chennai, India (email: [email protected]). 80 International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012 between the slopes of the ith and (i-1) th correction lines. Thus, the maximisation of γ ( pi | pi − 1) smoothes the transition of Consequently, the estimation of local slant angles can be treated as a problem of optimising the slope of the correction line at i = 1,….,M. Since the slope of the ith correction line is fully controlled by pi, the non-uniform slant correction problem is finally reduced to an optimization problem of p 1 ,…, p i ....,p M . p1 … pi-1 pi …. the local slant angles together with the constraint of (2).Several characters, in Tamil have long strokes with inherent slants. Those inherent slants may be “over-corrected” by the effect of si ( pi ) . The last function ci ( pi ) is introduced to avoid the overcorrection. The function ci ( pi ) evaluates the similarity between the local slant angle by pi and an average slant angle in a neighbourhood of the ith correction line. The average slant angle is calculated by averaging the slope of the character contour in the neighbourhood. Since the average slant angle is rather insensitive to the above inherent slants, ci ( pi ) is useful to suppress over-corrections. The function pM N Correction line 1 1 i-1 i also has an effect of stabilising the estimated local slants. The optimization problem of p 1 ,…, p i ,...,p M can be treated as an optimal path problem . The path starts from i = 1 and ends in i = N on the i—p i search graph. If the path passes through (i, p i ) next to (i-1, p i-1 ) on the search graph, we have the gain fi(pi|pi − 1 ) .Clearly, the sum of the gains along the M Fig. 2 Ancient Tamil character representation of non-uniform slant angles by correction lines Observation of text lines for designing an optimization criterion reveals that the local slant angles have two properties: (i) long near-vertical strokes tend to exhibit local slant angles clearly, and (ii) the left-to-right transition of the local slant angles is smooth. The variable p i should be optimised so that the slope of the ith correction line is similar to the slopes of long near-vertical strokes around the horizontal position i and the slopes of consecutive correction lines are similar to each other. According to the above discussion, the non-uniform slant correction problem is defined as follows: Maximize M (1) F(p1,...,pi ,...,pM ) = ∑ fi(pi|pi −1 ) i =1 With respect to p 1 ,…, p i ....,p M , subject to 0 ≤ pi − pi −1 ≤ 2, | pi −1 |≤ W , path becomes the objective function of (1). The shape and range of the path are regulated by the constraints (2) and (3), respectively. It is well known that the optimal path problem can be solved efficiently by dynamic programming. The algorithm proceeds from i = 1 to M (i.e., from left to right in the search graph). At each (i, p i ), the algorithm calculates (4) g (i , pi ) = [ fi ( pi | pi −1 ) + g (i −1, pi −1 )] max 0≤ pi − pi −1 ≤ 2 Where g (i − 1, pi − 1) ) is the maximum accumulated gain up to (i − 1, pi − 1) . This equation implies that the maximum accumulated gain can be calculated recursively. Thus, after the algorithm reaches i = M, we can obtain the maximum of the objective function as max pM g ( M , pM ) . The optimal path (2) (3) i.e., the optimal sequence p 1 ,…, p i ....,p M is obtained by tracing back the selection of p i-1 at p i . The slant corrected text line image is then obtained by the optimal sequence p 1 ,…, p i ....,p M . Specifically, the image is obtained by mapping pixels on the correction line between (i, 1) and (p i , N) linearly onto the vertical line between (i, 1) and (i, N). This mapping is formally represented as (( p − i ) j / N + i, j ) (i, j ) . is a function to evaluate a fi ( pi | pi − 1) “goodness” of the ith correction line specified by p i , and W is a positive constant. The details of fi ( pi | pi − 1) will be Where described below. Constraint (2) is imposed to smooth the transition of the local slant angles and constraint (3) to specify the range of local slant angles as [− tan −1(W / N ), tan −1(W / N )]. The function fi ( pi | pi − 1) is defined as a weighted sum of three functions, and si ( pi ), γ ( pi | pi − 1) ci ( pi ) .The first function III. OFFLINE ANCIENT TAMIL TEXT LINE RECOGNITION After these normalisation steps, a stone inscription text line is converted into a sequence of feature vectors. For this purpose a sliding window with a width of one pixel is used. The window is moved over the image from left to right, one pixel at each step. Nine geometrical features are extracted at each position of the window. The first three features contain the number of foreground pixels in the window and the first and the second order moment of the foreground pixels. Features four and five contain the position of the upper and si ( pi ) becomes larger if a longer stroke is on the correction line. Thus, by the maximisation of c , the slope of the ith correction line will become similar to the slope of a long nearvertical stroke, which will exhibit a local slant angle clearly. The second function γ ( pi | pi − 1) evaluates the similarity 81 International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012 the lower contour is stored in features number six and seven. The last two features contain the number of vertical blackwhite transitions and the pixel density between the upper and the lower contour. In the Hidden Markov Model (HMM) based recognition each character is modelled with a linear HMM. The number of states is chosen individually for each character [17], and twelve Gaussian mixture components are used to model the output distribution in each state. Based on the lexicon, word models are built by concatenating character models. The Baum-Welch algorithm is used for the training of the HMMs, whereas the recognition is performed by the Viterbi algorithm. A statistical bigram language model at the word level supports the Viterbi decoding step. The integration of this language model is optimised on a validation set as described in [17]. iii. Sort a i as sorted _ a i , calculate third quantile 3 Q3 = sorted _ ai , i = h × 4 iv. Trace number of pixel from first row to middle line for k =1..mid if ak ≥ Q3 then upperbaseline = k ; break; v. Trace number of pixel from h to mid for k = h..mid if a k ≥ Q3 then baseline = k ; break; B. Slant angle estimation and correction Slant correction is an indispensable technique for both holistic and analytical stone inscription word recognition [3]. Accordingly, a detailed review of uniform/non-uniform (local) slant correction techniques is presented in section 2. However, it is widely demonstrated in IAM benchmark database that slant of all characters composing a word is uniform. Therefore, most of the researchers dealt with uniform slant angle detection and correction [3, 4, 5, 12, 15, 16, 17].Hence, proposed technique is based on structural features of the first character and targets uniform slant angle estimation. This not only reduces computational complexity but also yields good results. To estimate slant angle, maxima and minima of first character are detected and connected by a straight line (See figure 2). The proposed algorithm for slant angle estimation is presented as under. IV. PROPOSED TECHNIQUES In this section, algorithms described have been tested on ancient Tamil stone inscription images. The black pixels (foreground pixels) in the binary word images represent the characters whilst the white pixels are used to represent background. A. Core-region Detection Our approach is based on enhanced horizontal density histogram embedded with quantile features to detect core region of stone inscription images. Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable. Dividing ordered data into q essentially equal-sized data subsets is the motivation for q-quantiles; the quantiles are the data values marking the boundaries between consecutive subsets. The proposed technique is slightly modified but it demonstrates to be more effective than [5] even in the presence of long horizontal strokes and erratic characters. Initially, middle line of the image is detected by constructing horizontal density histogram. The peak of horizontal histogram is the middle line. A threshold is calculated using quantile on the sorted data of horizontal histogram. In the horizontal histogram from first row to middle line, the line with count number of pixel greater or equal to threshold (third quantile) is detected as upper-base line. Similarly, from last row to middle line, the line greater than or equal to threshold is the baseline. Proposed algorithm is presented as below. Let say an image denoted by P where P ϵ{0,1} Let say an image denoted by P where P ϵ{0,1} i = 1,2..., h (8) pi , j ∈ P j = 1,2,..., w h, w is height and width of P respectively. i. Take left most p = pi , j | pi , j = 1 define it as p1 = ( x1 , y1 ) { || d1 ||= ( x2 − x1) 2 + ( y2 − y1 ) 2 i, j , i = 1,2,..., h (9) & d 2 as distance of p 2 – p 3 || d 2 ||= ( x3 − x 2 ) 2 + ( y3 − y2 ) 2 (10) v. calculate slope to set shear direction k (clockwise or anticlockwise) y − y1 , if m < 0 then k =-1 else k =1 ; (11) m= 2 x2 − x1 w ∑p } ii. Take first maxima from left most as p2 = ( x2 , y2 ) iii. set p3 = ( x2 , y1 ) iv. calculate d 1 as distance of p 1 – p 2 i = 1,2..., h (5) pi , j ∈ P j = 1,2,..., w Where h, w is height and width of P respectively. i. Compute number of foreground pixel each rows ai = (7) d vi. Calculate slant angle θ = k × sin −1 2 d1 vii. If θ >15 then shear P as (6) j =1 ii. Define middle line (mid) as index of max(a i ) mid = max(a i ) 82 (12) International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19-20, 2012 0 i sin(θ ) p' = θ ) j 0 cos( independent recognition, decreases. All differences are statistically significant at the 99% level. The results on the validation and test set are summarised in Table. 1. (13) VI. CONCLUSION It is mention worthy that proposed technique has no negative impact to non-slanted words and is equally suitable for the slanted characters slope either clockwise or anti clockwise. In this paper, new and enhanced preprocessing techniques TABLE I ACCURACY RESULTS ON VALIDATION AND TEST SET Validation Test Baseline System 78.2% 84.5% Non-Uniform Slant(Test) 80.5% 85.8% Non-Uniform Slant (Train & Test) 82.7% 89.7% V. EXPERIMENTS AND RESULTS All experiments reported in this section make use of the HMM based recogniser described in Sect. 3. The stone inscription text lines used to train validate, and test the proposed system originate from the IAM1 database [12]. The baseline system we use applies all steps of the first preprocessing phase described in Sect. 2, including skew, baseline and uniform slant correction. To test the effect of the proposed non-uniform slant normalisation we conducted two experiments. In the first experiment the trained HMMs of the baseline system are used to recognise the non-uniformly slant corrected lines in the validation and test set. The motivation is that the diversity among the character instances in the training set, which is an important issue in ancient Tamil character recognition [15], is higher if no non-uniform slant correction is applied. In the second experiment the HMMs are trained and tested using the text line images with non-uniform slant correction. The results on the test set are as follows. If we apply the non-uniform slant correction to the test set only and use the trained HMMs of the baseline system the recognition accuracy increases from 64.5% to 65.8%. Thus, we can benefit from the additional normalisation step to improve the performance of our handwriting recognition system. However, if the non-uniform slant correction is applied to both, training and test images, the word level accuracy drops to 62.7%. for off-line ancient Tamil character recognition have been presented. Enhanced technique for core-region detection exhibits better results than [5] generally and particularly for erratic words. All proposed techniques are tested on benchmark database of handwritten words. Accuracy rate up to 94% and 96% were found for lower baseline and upper baseline detection respectively. Finally, slant angle estimation and correction approach was successful in 95.74% cases. Simplicity of the proposed slant correction approach reduced computational complexity significantly. 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Pattern Recognition Letters, 22(9):1043–1050, 2001. [9] [10] [11] [12] [13] [14] [15] [16] S. RajaKumar,is a Research Scholar in the Department of Electronics and Communication Engineering in Sathyabama University, Chennai. He received his B.E in Electronics and Communication Engineering and M.E in Applid Electronics . His intrests include Image Processing, Signal Proccessing and Pattern recognition. Dr. V. Subbiah Bharathi graduated from Government College of Engineering,Tirunelveli in 1986, Master Degree from Regional Engineering College, Warrngal in 1994 and Ph.D from Manonmaniam Sundaranar University,Tirunelveli in 2005. He published more than 40 papers in International Journal and Conferences. His research intrests include Image Processing, Computer Vision and Pattern Recognition. 84