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1 Geometric Feature Extraction from Urdu Ligatures NAILA KHAN, AWAIS ADNAN 2, SADIA BASAR Department of Computer Science, Institute of Management Sciences, 1-A, Sector E-5, Phase VII, Hayatabad Peshawar, Pakistan ABSTRAC: -This research aims at the extraction of geometric features from Urdu ligatures. Though structural features are robust, its extraction and analysis is exceptionally complex and time-consuming task. The extraction and analysis is uncomplicated in case of the geometric features. Geometric features are language, script and font independent. There are twelve significant geometric features extracted from the ligature images. Specifically, these twelve features are the height, width, aspect ratio, density function, perimeter, area, perimeter to area ratio, horizontal projection profile, vertical projection profile, start point, end point and the slope between start and end point. Keywords: Features, Geometric, Ligature, Structural, Urdu 1. Introduction Urdu is the national language of Pakistan [1-3]. There are 60 to 80 million Urdu speakers across the world [4]. Urdu script is very similar to the Arabic script and is written in Nastalique calligraphic style [5-7]. Nastalique font is highly cursive and context sensitive in nature [2, 8-10]. This cursiveness of text presents numerous challenges in character segmentation[8, 11]. Urdu words are composed of ligatures and isolated characters (see Fig. 1). Ligature is a sub-component of a word and can also be regarded as a sub-word [6, 12]. 2. Urdu Script There are certain characteristics accompanying with the Urdu script. The sub-headings below summarize some vital characteristics of Urdu Urdu Language Writing System Arabic, Urdu and Persian are written in Perso- Arabic script. Therefore, they share great likenesses at the written level. Urdu also uses a revised and extended set of Arabic and Persian alphabets [13]. Any Urdu language learner will be capable of reading the Arabic script without the knowledge and understanding of its written meaning. As shown in Fig. 1 Urdu alphabet has a total of 38 characters [14]. Out of these 38 characters, 28 are similar to the Arabic alphabet [15]. The shape of a character varies in Urdu according to its position in the word and henceforward extremely context sensitive. A character has different shapes when written in isolation, start, middle or end of a word [15]. In Urdu a word is composed of ligatures and ligatures are combination of characters [16]. In addition, Blank spaces are not regarded as separation or boundary between words. Diacritic marks are used with its accompanying characters for proper pronunciation. Urdu is written in the famous Nastalique calligraphic style whereas Arabic is written in Nasakh font. Nastalique calligraphic style is very complex and context sensitive in nature [17]. Figure 1: Urdu character set 2.2. Urdu Script Composition An Urdu script is composed of the following sub components [16]. Isolated Character: It is a character in its isolated form. Ligature: It is composed of two or more characters. It is also known as sub-word. Word: It is composed of isolated characters and ligatures. Sentence: It is composed of a collection of words and isolated characters. Any component level can be used in script recognition systems. Word and ligature are mostly preferred because of the fact that character recognition introduces segmentation complexities [8]. ISBN:

2 2.3. Bi-Directional Writing System Urdu is bidirectional writing system. Urdu numbers are written from left to right while its words are written from right to left [18]. consonants. Dental consonant as shown in Fig. 5 are spoken when tongue is pressed against the upper teeth. Figure 5: Retroflex consonants Figure 2: Urdu script writing direction 2.4. Diacritics Urdu characters are surrounded by special type of marks known as diacritics. The diacritic surround the characters main body and lie above or below it. There are three types of diacritics, 1. Nuqta (Dot) 2. Aerab Superscript ط 3. The nuqta(s) placement and number is used to distinguish several characters in the Urdu alphabet. The nuqta(s) can be placed below or above the associated character. The nuqta(s) can range from one to maximum three in number. Total 17 characters in the Urdu alphabet are accompanied by the nuqta(s). Figure 3: Showing the dots (nuqtas) accompanied by Urdu characters Characters represent consonants and diacritics serve as vowel marks. Diacritics are also known as Aerab. Aerab helps in the pronunciation of Urdu characters. Aerab are optional and written with the Urdu script when there is need to remove any confusion in the pronunciation [18]. The Aerab helps in changing the sound of the letter (see Fig. 4). Figure 4: Aerab in Urdu language Retroflex consonant is spoken when the tongue has a curled, flat or concave shape. These were not present in the Persian or Arabic alphabet. Three characters in Urdu are known as retroflex consonants. The retroflex consonants are created by placing the ط superscript on three Urdu characters. These Urdu characters are known as dental 3. Structural Features 3.1. Structural Features Depiction Structural features include the topological structure of the characters [19]. The characters can be defined by its morphological/ structural features such as start point, end point, branch point, cross point, branches, filled simple loop, open loop, double/complex loop, hedges, cusp etc. [17-22]. Structural features describe the corporal makeup of the characters (see Fig. 6). The structural features are highly effective for recognition and classification purposes [23, 24] Complications Associated With Structural Features There are numerous complications associated with the structural features. These complications are described below. Extraction of structural features is extremely complex task [24]. There are no general rules or approach set for extraction of structural features [24]. Structural features can be extracted from the skeleton of different symbols or characters [21, 24, 25]. Structural features have very less tolerance to rotation [24]. The designers and programmers are placed in an uncomfortable situation since there are no general rules for structural features identification and extraction. For each character, the features related with it are unknown and not fully defined. There is a lot of overhead in knowledge acquisition if the structural features are not established in advance [24]. Development of classification and recognition system that practices structural features is challenging. ISBN:

3 Figure 6: Structural features of some Urdu characters 4. Pre-Geometric Features Extraction 4.1. Corpora For the purpose of geometric feature extraction, 2430 most frequently used Urdu ligatures were collected. Due to deficiency of time for the current research, the ligatures were directly collected from the center for language engineering website [25]. Center for language engineering is an organization aimed at conduction research and development in various regional languages of the Pakistan [25]. The ligatures were extracted from 19.3 million corpuses according to the center for language engineering. The domain from which the corpus was extracted includes, sports/games, news, finance, culture/entertainment, consumer information and personal communications [25] Step-wise Solution For Geometric Feature Extraction 1. All 2430 ligatures are organized within bitmap images where each bitmap hold 21 or fewer ligatures arranged (see Fig. 7). 2. Each bitmap image is converted into pure black and white color. The ligatures i.e. the foreground are presented by white color while the black pixels present the background. The conversion into foreground and background is achieved using thresholding. 3. The images are segmented horizontally and vertically using horizontal projection profile and vertical projection profile respectively. Horizontal and vertical projection profile generates segmented ligature images. 4. For removal of unwanted pixels from the top and bottom of the ligature image, trimming is carried out. The result of trimming generates ligature images fit for geometric feature extraction. 5. The final shape of each ligature image is rectangular since image is two dimensional as shown in Fig. 7. Hence, geometrical features for a rectangle shape are measured and extracted. The features extracted from each ligature image are height, width, area, perimeter, aspect ratio, density function, ratio of area to perimeter, horizontal histogram, vertical histogram, start point, end point and slope between the start and end point. a. Height, width, area, perimeter, aspect ratio, density function, ratio of area to perimeter are extracted from the trimmed ligature image. b. The trimmed ligature images are resized to 32 x 32 before extracting horizontal and vertical histogram feature. c. Prior to the extraction of start and end point, all of nuqtas (dots) are removed from the ligatures images. Once nuqtas are removed, images are retrimmed. The start and end points are located and the slope between start and end point is found. a) Sample image b) Thresholded image Figure 7: Sample of segmented and trimmed ligature images 5. Geometric Features Proposition The proposed method does not take into account the detailed facts of the ligatures strokes and its structure. The proposed geometric features are extremely simple to extract and analyze. The geometric features suggested for extraction are, 1. Width: Measurement from side to side of ligature image. 2. Height: Measurement for the tallness of ligature image. 3. Aspect ratio: Ratio of height divided by width of a ligature image. 4. Density function: Total number of pixels covered by the ligature stroke within a ligature image. 5. Perimeter: Sum of sides of a ligature image. 6. Area: Product of width and height. 7. Perimeter to area ratio: Division of perimeter by area of ligature image. ISBN:

4 8. Horizontal projection profile: Sum of pixel intensities along each row in a ligature image. 9. Vertical projection profile: Sum of pixel intensities along each column in a ligature image. 10. Start point: The first pixel scanned from top to bottom at the left hand side border of the ligature image, where the ligature stroke touches border. 11. End point: The first pixel scanned from top to bottom at the right hand side border of the ligature image, where the ligature stroke touches border. 12. Slope between start and end point: The slope of the diagonal line when connecting the start point with the end point. 6. Geometric Features Extraction 6.1. Width Width is measurement for a geometric shape from one side to other side. Each ligature image takes different amount of space horizontally as shown in Fig. 8. Equation (1) is used for calculating the width for each segmented ligature image. (1) 6.3. Aspect Ratio The aspect ratio is defined as the measure of steepness of a line. The line connects two points on the coordinate plane. In other words, the aspect ratio of a line is the ratio between y and x. The value of y increases as the value of x increases in some amount. The aspect ratio of a line remains constant anywhere on the line. For aspect ratio of a ligature image the combination of two values i.e. height and width is calculated. Aspect ratio is sometimes also known as the slope. (3) Refer to (3) to know the relationship between height and width that is used to find the aspect ratio of the ligature image. Fig. 10 shows the ligature images with the least aspect ratio. Fig. 10 (a) has the least aspect ratio out of all the 2430 ligatures analyzed. If Fig. 11 is analyzed from right to left, the aspect ratio is increasing. Figure 8: The width of few segmented and trimmed Urdu ligatures 6.2. Height Height is the measurement of tallness. Each ligature is formed from characters of varying heights. Once the ligature is trimmed the height can be calculated. Refer to (2) to find the height for a ligature image. Figure 10: Ligature images with least aspect ratio Fig. 11 shows the ligature images with the maximum aspect ratio. Fig. 11 (a) to Fig. 11 (e) displays the maximum aspect ratio for different ligatures in descending order respectively. The ligature images having the maximum aspect ratio have slopes that are almost vertical along the y-axis. (2) Fig. 9 shows the height feature for few Urdu ligature images. Both height and width are vital geometric features. Other geometric features can be found by identifying relationship between these two features. Figure 11: Ligature images with maximum aspect ratio Figure 9: The height of few segmented and trimmed Urdu ligatures 6.4. Density Function The density function is used for finding distribution of total number of pixels in the image to the pixels covered by the main character in the image. ISBN:

5 Equation (4) is used for calculating the density function for a ligature image Perimeter (4) Perimeter is defined as the sum of all sides of a geometric shape or polygon. It is the measure of the length of a shape around its outermost extremities (see Fig. 12). Finding the perimeter for any quadrilateral is very simple; it is even simpler if the sides are of equal length. When dealing with squares since all side are of equal length, it is easier to calculate the perimeter. For rectangles, the perimeter can be using (5). (5) Rectangles have two sides with different lengths i.e. x and y and therefore the perimeter is equal to 2x + 2y (x and y can be referred to as the width and height). A rectangle can be a square but a square can never be a rectangle Perimeter to Area Ratio The perimeter to area ratio is the area divided by its perimeter, refer to (7). The perimeter and area calculated in earlier sections are used to calculate the perimeter to area ratio. = 6.8. Horizontal Projection Profile (7) Histogram is a graphical representation showing the distribution of data values [15]. It is used to show the distribution of pixels in a ligature image horizontally. The horizontal histogram consists of peaks and valleys showing the distribution of pixels in ligature image horizontally. In the proposed research an important step is performed before finding the horizontal and vertical projection profile. The entire ligature images are resized to 32 x 32. Fig. 15 shows the horizontal and vertical projection profile calculated for the ligature faen. Figure 12: The perimeter calculated for some Urdu ligature images 6.6. Area Area for any shape is the total amount of space that it covers. The ligature images are in rectangular shape. The area of a rectangle can be easily calculated by multiplying the height and width of the rectangle as shown in (6). Fig. 14 shows the perimeter calculated for four ligature images. (6) Figure 13: The area calculated for some Urdu ligature images Figure 14: Horizontal and vertical projection profiles for ligature faen. For horizontal projection profile, the image rows are scanned from top to bottom. For each row the sum of its pixels are calculated. When a row contains many white (on) pixels it is represented by a peak in horizontal projection profile. If all pixels in a row are black, (off) it represent the background and is displayed as a valley in horizontal projection profile. Once the horizontal histogram is computed, the maximum (peak) value of the histogram is found. The row index, which has the maximum (peak) value, is utilized as a geometric feature for classification and recognition purposes. If more than one row has the same maximum (peak) value, then the first row from top to bottom is selected as a feature. For ligature image faen, which has 32 ISBN:

6 rows and 32 columns, the row index selected as geometric feature is shown in Fig. 16. Figure 16: Steps for finding the start point, end point and slope between them Figure 15: Horizontal projection profile values along with rows index for ligature faen In Fig. 15 the maximum peak value for horizontal histogram is 15. In addition, the row of the image, which holds this peak value, is 28. Hence, 28 row index is utilized as the geometric feature for horizontal projection profile of the ligature image fean Vertical Projection Profile Vertical histogram for any ligature image is computed after taking the sum of pixels along each column [15]. Vertical projection profile is computed similarly to the horizontal projection profile but with one difference i.e. along the columns. The vertical projection profile for ligature fean can be seen in the Fig Start Point Start point and end point cannot be easily located in images having ligatures accompanied by nuqtas (dots). These nuqtas lead to erroneous data therefore the dots are removed from the ligature images. For finding the start end points as well as the slope between them, these steps were followed. 1. Nuqtas (dots) are removed from the image. 2. The image was trimmed for removal of unwanted pixels. 3. The final trimmed image is resized to 64 x Start and end points are extracted and the slope is calculated. Fig. 16 depicts the necessary steps taken for identification of start point, end point and the slope between them. In Fig. 17 the directional arrow shows that steps were carried out from left to right. Start point and end point are two important features. To calculate the start and end point and find its slope the ligature images are considered to be in the Cartesian coordinate plane (see Fig. 17). Where X covers the columns of the image and Y covers the rows within the image. Figure 17: Cartesian coordinate plane assumed for ligature image Start point is calculated as the pixel point in image which has value of 1(white) and is in the left most column of the ligature image. This point is actually the pixel point where the ligature stroke touches the left hand side border of the image. If ligature stroke touches the left hand side border at multiple points, the column is analyzed from top to bottom and the first pixel found with value 1 is taken into consideration. The position of the start point is found with pixel having coordinates, P(X1, Y1). Y1 is considered the row value and X1 the column value. The value of X1 is fixed to 1. The first column in the ligature image is scanned from top to bottom, the point where a pixel is on i.e. 1 (white) is taken as value for Y1. The intersection of X1 and Y1 gives us the required pixel P(X1, Y1).The start point for a ligature image can be seen in Fig. 18. The value of Y1 is used as geometric feature and stored for future reference. ISBN:

7 An important point here is that we may get a negative slope. The slope of the line is considered negative when the line was slanting upward from the left to the right (see Fig. 20). The slope is considered positive when the line was slanting downward from left to right (see Fig. 21). Figure 18: Finding start point for a ligature image End Point End point is calculated as the pixel point in image which has value of 1(white) and is in the right most column of the ligature image. This point is actually where the ligature touches the right side border of the image. If a ligature touches the border at multiple points, then the column is analyzed from top to bottom and the first value found is taken into consideration. The position of the pixel is found as P(X2, Y2). The value of X2 is fixed to 64. The last column of the image is scanned from top to bottom and the point where a pixel point is on i.e. 1(white) is the value of Y2. The combination of P(X2, Y2) gives the pixel where the ligature touches the right hand side border of the image. The value of Y2 is used as a geometric feature. The end point for a ligature image can be seen in Figure 19. Figure 19: Finding end point for a ligature image Slope between Start Point and End Point A straight line is created when the start point is connected to the end point. The slope of the line is found using the values X1, Y1, X2 and Y2 [26]. Equation (8) shows the formula for finding the slope between start and end point. The formula is the similar to the slope that we have used before i.e. change in height divided by width. The numerator takes into account the difference of Y values while the denominator takes into account the difference of X values. (8) Figure 20: Negative slope for a ligature image Figure 21: Positive slope for a ligature image To avoid negative values in the slope, absolute of difference between Y1 and Y2 and absolute of difference between X1 and X2 is taken into account before finding the slope. 7. Conclusion Urdu is extremely cursive and context sensitive language. Extracting structural/topological features is challenging, complex and time consuming. The geometrical features proposed are easier to understand, analyze, extract and utilize. These features can be used in various pattern recognition systems software s such as optical character recognition system. References [1] T. Nawaz, S. A. H. S. Naqvi, H. ur Rehman, and A. Faiz, "Optical character recognition system for urdu (naskh font) using pattern matching technique," International Journal of Image Processing (IJIP), vol. 3, p. 92, [2] S. T. Javed, S. Hussain, A. Maqbool, S. Asloob, S. Jamil, and H. Moin, "Segmentation free nastalique urdu ocr," in Proceedings of World Academy of Science, Engineering and Technology, 2010, pp [3] S. Hussain, "Resources for Urdu Language Processing," in IJCNLP, 2008, pp [4] S. Sardar and A. Wahab, "Optical character recognition system for Urdu," in Information and Emerging Technologies (ICIET), 2010 International Conference on, 2010, pp ISBN:

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