International Journal of Machine Learning and Computing, Vol. 2, No. 5, October 2012 Finding Critical Points of Handwritten Persian/Arabic Character Majid Harouni, Dzulkifli Mohamad, Mohd Shafry Mohd Rahim, and Sami M. Halawani and recognizing the output of feature extraction as its inputs; Abstract—On-line handwritten character recognition system there exists some useful techniques for classification stage is a special line of research in image processing and pattern such as: neural networks, fuzzy logic, etc. So, the significant recognition field, it can also considered a special process of in part is to achieve a most desirable data set form the academic researches and production fields in the past decade. hand-drawn letters, i.e. in the pre-processing. The The general steps in a recognition system are pre-processing, segmentation, feature extraction and classifications. The polynomial approximation was used by singular value techniques of pre-processing stage play an excessive role in the decomposition of on-line handwriting coordinates [1]. In [2], system and directly affect the system performance. The focus of the pre-processing was to obtain a data acquisition by this article is on doing pre-processing stage and providing a smoothing, size normalization and resampling. The decision most desirable data set form the raw data using in feature tree was proposed from the list of a string of the X and Y extraction and then to increase the rate of recognition system. coordinates for data collection set [3], and smoothing For this reason, the novel algorithm is presented for finding a critical points set of hand-drawn letters. These critical points averages of these coordinates done with neighboring pixels, help extracting the correct structural and statistical features of then filtering by eliminating very close points as a minimum on-line character handwriting in any given style writing. distance between consecutive points [4]. In this case, we present a novel algorithm to find these critical points of each Index Terms—Persian and Arabic script, critical points, isolated letter using feature extraction; this algorithm based on-line character recognition, pre-processing. on the ratio of some point set in either both sides of the critical points, which abstains from using of smoothing or resampling and normalization techniques. I. INTRODUCTION This paper is presented as follows: section 2 exhibits the Pre-processing of the on-line handwriting recognition overview of the Persian and Arabic script. The system has always been challenging and worthwhile subject, pre-processing stage uses the proposed algorithm in section 3. and also gained much attention in recent years. In fact, the Section 4 reviews the common feature set of on-line Persian on-line handwriting recognition is one of the most successful and Arabic handwriting. Section 5 is devoted to the and scientific applications of image processing and pattern experimental and test results of the algorithm. The paper recognition. The common architecture of this system consists terminates in the concluding remarks in section 6. mainly of four stages: pre-processing, segmentation, feature extraction and recognition or classification. Pre-processing promotes the hand-drawn letter or raw data into a desired II. PERSIAN AND ARABIC SCRIPTS data set using in all other stages; the procedures of image Persian and Arabic are written from right to left. The processing may be used for this stage, such as interpolation, cursive natures of the scripts, styles of writing are their smoothing, normalization, etc. The stage of segmentation is inherent problems. The Persian letters are four letters more designed to divide the words/sub-words, and letters into their ;and 32 letters ,’پ‘ and ’گ’, ‘چ’, ‘ژ‘ ,than Arabic script characters and some particular parts respectively. Based on generally, some Arabic vowel sings (hamza, shadda, etc) do the first two stage (or one of them in terms of designing not use in Persian writing, The Persian/Arabic letters have up system), the structural and statistical features will be to four different shapes, depending on their preceding and extracted. Finally, a classifier is appointed for categorizing position in the word; for example character “Heh”: isolated In sum, Table 1 .”ه“ and final ,”ھ“ middle ,”ھ“ initial ,”ه“ Manuscript received May 25, 2012; revised September 18, 2012. This illustrates that all of Persian/Arabic alphabets can just be ا’, ‘ research is supported by the Ministry of Higher Education (MOHE) and connected from both sides; but only these seven letters collaboration with Research Management Center (RMC) Universiti can be connected to the ’و‘ and ’‘د’, ‘ذ’, ‘ر’, ‘ز’, ‘ژ Teknologi Malaysia (UTM). This paper is financial supported by GUP Grant (NO. VOT: Q.J130000.7128.01J18). former-letter from the right side. Thus, these letters Majid Harouni is with UTMViCube Lab, Faculty of Computer Science proximate cause discontinuity within the same word as its and Information Systems, Universiti Technologi Malaysia, Johor, Malaysia and with the Department of Computer Science, Islamic Azad University, sub words. Several of Persian/Arabic alphabets divide by the Dolatabad branch, Isfahan, Iran (e-mail: majid.harouni@ gmail.com). same main body and can be different only by a small Dzulkifli Mohamad and Mohd Shafry Mohd Rahim are with the completing part such as some dots, and some slanted lines. UTMViCube Lab, Faculty of Computer Science and Information Systems, Moreover, Fig. 1 illustrates apart from dotting in alphabet, Universiti Technologi Malaysia, Johor, Malaysia (e-mail: [email protected], and [email protected]). most letters are written in one stroke, only five Persian letters Sami M. Halawani is with the Faculty of Computing and Information require write two or more strokes. Technology, King Abdul Aziz University, Saudi Arabia (e-mail: As Table I is shown that there exist 18 of the 32 Persian [email protected]). 10.7763/IJMLC.2012.V2.192 573 International Journal of Machine Learning and Computing, Vol. 2, No. 5, October 2012 letters have dots that appear on above or below of them. TABLE II: NINE-GROUP OF BODY CHARACTER IN PERSIAN ALPHABET Exactly, there are 10 letters of them have one dot, 3 letters G. 1 G. 2 G. 3 G. 4 G. 5 G. 6 G. 7 G. 8 G. 9 د have two dots and 5 of them have three dots. The number of س ج ب dots of in each letters does not change with its four different ذ ف ک ع ط ش چ پ ا that has no dot in isolated’ی‘ forms, excepting the letter of ر ق گ غ ظ ص ح ت آ and final shapes, and instead two dots in its initial and middle ز ض خ ث shapes; hence, any addition or deletion of the dots or slanted ژ .lines can product a misinterpretation of the letters Correspondingly, there are many different styles of writing in Persian/Arabic which introduce different allograph for letters or some letter combinations. As Table 2 shows, there exists III. PRE-PROCESSING AND FINDING CRITICAL POINTS the nine-group of similar body character in Persian alphabet; The primary purpose of the pre-processing stage is to some letters only have same body in initial and middle shapes provide desired data input collection from the raw data via in which these on-line hand-drawn letters for other stages. The ,’ن‘ or the letter of ’ف‘ such as the letter of shapes can be considered same as second group. pre-processing stage includes sub stages such as interpolating or smoothing, resembling and etc. We make a decision based TABLE I: THE PERSIAN ALPHABET IN THE FOUR DIFFERENT SHAPES on which sub stages of pre-processing are useful and most Character Transliteration Isolated Initial Middle Final likely to utilize. So, it is clear that the time of recognition of .letters diminish by decreasing and limiting these sub stages ا ,ا ا ,ا آ, ا آ, ا Alef a We present the proposed pre-processing stage without any ب ب ب ب Beh b ,smoothing and resembling the hand-drawn letters پ پ پ پ Peh p segmentation of each hand-drawn letters into some strokes ت ت ت ت Teh t and dividing each of these strokes into sub-stroke as several ث ث ث ث Theh Th tokens (each sub-stroke is called one token) by the following ج ج ج ج Jeem j unique algorithm in coming sub section. چ چ چ چ Cheh ch A. Finding Critical Points and Dividing Stroke ح ح ح ح Heh h This sub section is based on our unique algorithm to divide خ خ خ خ Kheh kh one stroke into several different parts, which are called د د د د Dal d tokens. Utilizing this algorithm can aid us to find the corner ذ ذ ذ ذ Thal th points that mean local maximum and minimum points of each ر ر ر ر Reh r stroke. These points use for starting and ending point of each token. Our proposed algorithm is separated into four parts ز ز ز ز Zeh z (based on the hand-drawn letter is sequenced to a list of (x,y ژ ژ ژ ژ Zheh zh coordinate points as an input data set, where x and y س س س س Seen s represents the position of the selected points in terms of the ش ش ش ش Sheen sh ratio of the user’s speedwriting to whole length of the ص ص ص ص Sad s hand-drawn letter on a surface writing or tablet pc. ض ض ض ض Zad th Firstly, as noted already, the maximum number of strokes ط ط ط ط Tah t of Persian letters is up to 3 strokes. On the other hand, as ,Table 1 shows if each dot of letter is considered as one stroke ظ ظ ظ ظ Zah z then the maximum number of stroke is up to 4 strokes for ع ع ع ع Ain a every letter.