Towards Automatic Recognition of Fonts using Genetic Approach

Transcription

1 Towards Automatic Recognition of Fonts using Genetic Approach M. SARFRAZ Department of Information and Computer Science King Fahd University of Petroleum and Minerals KFUPM # 1510, Dhahran 31261, Saudi Arabia. S. A. RAZA Department of Management and Information Systems King Fahd University of Petroleum and Minerals Dhahran 31261, Saudi Arabia. Abstract: - An algorithm has been designed that models the outlines of handprinted and electronic fonts. By capturing the input through a scanning device, the bitmapped image is converted to a more flexible format by means of a mathematical description. This in turn reflects the input font through a series of non-uniform rational B-spline segments, pieced together via an acceptable level of continuity. An optimal solution is realised by the method being applied using a genetic approach. The algorithm has been tested on a number of symbols. Key-words:- Font, Significant points, Contour, Gray-level image, Outline, Spline 1 Introduction There are two fundamental techniques for storing fonts in computers [11]. One is bitmap and the other is outline. Outline representation of fonts has many advantages over bitmap representation. Multiple sizes may be derived from a single stored representation by suitable scaling. Different type faces can also be derived e.g. italics may be derived by shearing the original outline. Outlines may be arbitrarily translated, rotated, scaled, and clipped. Therefore most of the cotemporary desktop publishing systems are based on outline fonts. In the traditional approaches [3, 5, 9-12, 14], initially, a character is scanned from paper to obtain a gray level image. From this gray-level image, contour of the character is obtained. Then corner points of the character are determined from contour. These corner points can be obtained by some interactive method or by some automated process. Optimal curve fitting is done by segmenting the contour outline at the corner points. Normally, the curve fitting methods are based on conics or Bezier cubics [6]. The algorithm proposed, in this study, mainly differs to the traditional approaches in various ways. It utilizes the extensions of various existing algorithms as its essential components. These algorithms include boundary detection, corner detection, spline fitting and genetic algorithms. Since, some times corners are not detected precisely and some times only corner points are not sufficient to fit the outline which represents the original character. Therefore, in addition to corner points, some more points are needed to achieve a best fit. This paper, like [15-16], suggests the extension of corner points to a super family of Points called significant points. These significant points are dynamically discovered using a genetic algorithm while the corner points remain static throughout the genetic process. The significant points play important role in the overall shape of the final character. Optimal curve fitting is done, not by segmenting the contour outline at the significant

2 points, rather a global spline model is fitted in a genetic way. The spline model used, in this work, is a non-uniform rational B-spline (NURBS), which has attracting geometric features to those in [15-16] where non-uniform B-spline (NUBS) were used. The organization of the paper is made as follows. The discussion summary of scanning the image and filtering the noise in it is made in Section 2. This Section is also meant for the boundary detection algorithm to the scanned image. The issue of detecting the characteristic points (corner points and significant points) is summarized in Section 3. A mention of NURBS model is made in Section 4. Gnetic Algorithm is discussed in Section 5. The Section 6 discusses the issue of best possible boundary fit. The Section 7 concludes the paper. 2 Image Outline Extraction The extraction of the contour points, from the gray-level image, is the first step of the whole process of Font recognition. During the digitization process (converting the gray-level image to a bilevel image), some noise may arise. This adds irregularities to the outer boundary of the image and, hence, may have some undesired effects. The algorithm of Avrahami and Pratt [2] is a reasonable solution to convert the gray-level image to a bilevel image. Although, this algorithm minimizes the error during the conversion process but it requires some modifications, which have been incorporated in this theory. The algorithm returns number of boundary points and their values: F(i) = (x(i),y(i)), i=1,...,n. 3 Detection of Significant Points After finding out boundary points, the next step in preprocessing is the detection of corner points. The corner points are those points, which partition the outline into various segments. There has been a good amount of work done for the detection of corner points given the boundary of an image. A number of approaches have been proposed by researchers [4]. These include Curvature analysis with numerical techniques and some signal processing methods. In [4] some of the possible ways to detect corners in an image are presented. The curvature can be analyzed using some numerical approaches. The algorithm, in [10], has used the numerical approach. But it presents a problem of scaling. The detection of corner actually depends on the actual resolution of the image and processing width to calculate the curvature. For the detection of corners, in this paper, we adopted the simple technique presented by Chetverikov and Szabo [4]. Their algorithm depicts the corners intelligently. The algorithm has been adopted with certain specific degrees of freedom, in its description, so that best possible results are achieved for our objectives. Since corner points are not always enough to produce the outline of the character. Therefore, in addition to corner points, we identify some more points, called significant points. In the first step, these points are searched on the basis of computation of high curvatures in each segment of contour outline. If these points do not happen to provide optimal outline, the second step is adopted to get further significant points. A genetic approach is discovered, for this purpose, which searches for the best significant points so that the curve fit is optimal to the contour of the character. The set of significant points is a super set of the set of corner points. 4 Spline Model There are various techniques, for curve design, available in the literature [6, 17]. The spline model used, in this work, is a non-uniform rational B-spline (NURBS), which has attracting geometric features [6]. The theory of the proposed study is based upon the NURBS of arbitrary degree. However, a cubic NURBS is recommended for practical purposes. 5 A Brief Overview Of Genetic Algorithm In the last decade, Genetic Algorithms [7] have emerged as practical robust optimization search

3 methods. Genetic Algorithms (GAs) are search techniques based on the concept of evolution. Given a well-defined search space in which each solution is represented by a bit string, called a chromosome, a GA is applied with its three genetic search operators (selection, crossover and mutation) to transform a population of chromosomes with the objective of improving the quality of the chromosomes. The individual bits of a chromosome are called genes. Before the search starts, a set of chromosomes is randomly chosen from the search space to form the initial population. The three genetic search operations are then applied one after the other to obtain a new generation of chromosomes in which the expected quality over all the chromosomes is better than that of the previous generation. The process is repeated until stopping criterion is met (for example, a predefined number of generations are processed). In the end the best chromosome of the last generation is reported as a final solution, as shown in Figure 1. scheme is not to lose those points as they are important in determining the outlines of the shapes. 6 Outline Capture The scheme used, in this study, converts the original continuous problem into a discrete optimization problem. Each data point (boundary point) corresponds to a single gene in the bit string of a chromosome. This technique, in principle, is similar to those used in [8, 13, 15-16]. In this formulation if a gene is equal to 1, we select the corresponding data point as a knot point of the spline. If the gene is equal to 0, the corresponding point is rejected (see Figure 2). All the selected knot points will be considered as the characteristic points. If the given points lie in the interval [a, b], then the appropriate number of knots are determined in the interval (a, b) called the interior knots n. The initial population, consists of K individuals of gene length L. The genes are randomly set to 0 and 1. However, the corner points are determined before the creation of initial population and the genes corresponding to those points are intentionally set to 1 in the initial population and in the population of the subsequent generations. The idea behind this Figure 1: Outline of a Genetic Algorithm. In addition to the conventional genetic control parameters (crossover and mutation), another control parameter knot ratio R is also used. Akaike's Information Criterion (AIC) [1] is used as a fitness measure. By using AIC, we can choose the best model among the candidate models automatically. AIC is given by

4 Figure 2: Individual Chromosome: Genetic Formulation. AIC = N log e Q + 2(2n+m) where, N is the number of data, Q is given as follows: Q = N j= 1 w j { S( t ) F } n is the number of interior knots, m is the order of the NUBS S(t) to be fitted on the given data F, and w j is the weight of data, taken to be 1 { } j for all data points in our case. It should be noted that the smaller value of AIC gives better fitness. A parameter for decimation is also proposed.. This parameter enables the data to be selected interval wise, without loosing the contour of the font as well as the significant points determined by the corner detection algorithm. This has been used in order to decrease the gene length of the chromosomes. In the context of genetic algorithm, a Roulette wheel selection and a double point crossover has been used. The probability of crossover C is taken to be 0.7 and the probability of mutation M is taken to be 0.001, while 0 < R < 0.5 has been used. The summary of the algorithm is given below. 1. Scan and filter the Font to remove any noise that might be present in the scanned image. 2. Apply Boundary detection algorithm to the scanned image to estimate the contour. 3. Determined the corner points from the contour of the Font. 4. Create an initial population at random. 5. Input the control parameters. j j 2, 6. For each individual in the population make the bits corresponding to the significant points as For each individual compute data fitting and obtain the fitness value. 8. If total number of generations exhausted, stop the computation, otherwise go to step Do selection by using the fitness values. 10. Do crossover and make the individuals of the next generation. 11. Do mutation and go back to step 6. The results, in Figure 3, are obtained by applying the algorithm an Arabic word 'Ali', see Figure 3(a). This word has been approximated with a cubic parametric B- Spline. The method, however, does not depend on the degree of the spline. For the Arabic word 'Ali', 411 points were determined by the boundary detection algorithm (see Fig 3(b) to see the outline obtained). The corner detection algorithm detected 12 corner points (see Fig 3(c)). The genetic algorithm was run for 120 generations with a population size of 30. The selection was based on Roulette wheel. The knot Ratio was 0.3 and the crossover and mutation rates of 0.7 and 0.001, respectively, were used. The algorithm converged at 98th generation (see Fig 3(d)). The bullets are the significant points detected by the algorithm. Other results for various symbols and the letters, for distinct languages, could also been shown. The Figure 4(a), 4(b), and 4(c) demonstrate the results of the algorithm at 25 th, 50 th, and 75 th iterations respectively.

5 (a) (b) (c) (d) Figure 3: The algorithm tested for an Arabic word 'Ali' (a) (b) (c) Figure 4: The algorithm tested at various iterations.

6 7 Concluding Remarks A method for font designing has been presented which is suitable for both Roman like as well as non-roman languages like Arabic, Gothic, etc. In addition to the detection of corner points, a strategy to detect a set of significant points is also explained to optimize the outline. A NURBS model, through a genetic approach, has been utilized to identify the significant points and hence capture the outline of the fonts. The proposed approach minimizes the human interaction in obtaining the outline of original character. This research is, in addition to the automatic capture of Fonts, is equally good to capture hand-drawn images. The authors feel that the proposed approach has the potential to be enhanced and make more automated and robust treatment using parallelism towards the computing aspect. Therefore, such a work is still in progress and the authors are expecting some more elegant results. The authors feel that the methodology using genetic algorithm and NURBS, proposed in this research, will prove to be quite reasonable as compared to the existing methods. However, a comparative study of the proposed research with the existing methods is needed to be made practically. Acknowledgement:- This work has been supported by King Fahd University of Petroleum and Minerals. References: [1] Akaike, H. (1974) A new look at the statistical model identification. IEEE Trans. Automatic Control, 19(6), [2] Avrahami, G., and Pratt, V., (1991), Sub-Pixel Edge Detection in Character Digitization, Raster Imaging and Digital Typography II, Eds., Morris, R. and Andre, J., Cambridge University Press, [3] Chang, H., and Yan, H., (1998), Vectorization of Hand-Drawn Image using Piecewise Cubic Bezier Curves Fitting, Pattern Recognition, Elsevier Science, Vol. 31(11), [4] Chetverikov, D., and Szabo, Z. (1999) A simple and efficient algorithm for detection of high curvature points in planar curves. Proc. 23 rd Workshop of the Australian Pattern Recognition Group, [5] Davis, L., (1979), Shape Matching using Relaxation Techniques, IEEE Trans. PAMI, Vol. 1, [6] Farin, G. E Curves and Surfaces for Computer Aided Geometric Design, Academic Press, New York. [7] Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley. [8] Harada, T., Yoshimoto, F., and Aoyama, Y. (2000) Data Fitting using a genetic algorithm with real number genes. Proc. of the IASTED International Conference on Computer Graphics and Imaging. [9] Hersch, R. D., (1994), Towards a Universal Auto-hinting System for Typographic Shapes, Electronic Publishing, Vol 7(4), [10] Itoh, Koichi, (1993), A curve fitting algorithm for character fonts, Electronic Publishing, Vol. 6(3), [11] Karow, P., (1994), Font Technology (Methods and Tools), Springer-Verlag, Berlin. [12] Mahmood, S. A., (1994), Arabic Character Recognition using Fourier Descriptors and Character Contour Encoding, Patter Recognition, Elsevier Science, Vol. 27(6), [13] Moriyama, M., Yoshimoto, F. and Harada, T. (1998) A method of plane data fitting with a genetic algorithm. Proc. of the Iasted International Conference on Computer Graphics and Imaging, pages [14] Sarfraz, M., and Khan, M. A. (2001), Automatic Outline Capture of Arabic Fonts, Journal of Information Sciences, Elsevier Science Inc., Vol. 140(3-4), [15] Sarfraz, M, and Raza, A, (2002), Visualization of Data using Genetic Algorithm, The Proceedings of the Fourth KFUPM Workshop on Information and Computer Science: Internet Computing (WICS 2002), Saudi Arabia, ISBN: , [16] Sarfraz, M., and Raza, S. A., (2001), Capturing Outline of Fonts using Genetic Algorithm and Splines, The Proceedings of IEEE International Conference on Information Visualization-IV 2001-UK, IEEE Computer Society Press, USA, [17] Safraz, M., (1994), Cubic Spline Curves with Shape Control, Computers & Graphics, Elsevier Science, 18(5),