2009 International Conference on Emerging Technologies

Transcription

1 2009 International Conference on Emerging Technologies A Self Organizing Map Based Urdu Nasakh Character Recognition Syed Afaq Hussain *, Safdar Zaman ** and Muhammad Ayub ** afaq.husain@mail.au.edu.pk, safdaraslam@yahoo.com, ayub_iiui@hotmail.com * Department of Computer Science, Air University Islamabad, ** Department of Computer Science, International Islamic University, Islamabad Abstract Research in the field of character recognition for Urdu script faces challenges mainly due to its characteristics, like cursive nature, multiple fonts and context dependent shapes of characters and their position with respect to the base line. This paper addresses problems recognizing Nasakh script of Urdu Language. The proposed system takes segmented character as input and recognizes them in two steps. In the first step the different shapes of each character are classifies into 33 categories using Kohonen Self-organizing Map (SOM) by auto clustering similar ligatures for initial classification. During the Feature Extraction phase more than twenty five different features are extracted from each character which are further processed for final character recognition. Keywords-Offline Character Recognition, Urdu Nasakh, Clustering, Self Organizing Map (SOM), Neural Network. I. INTRODUCTION Often abbreviated OCR, optical character recognition refers to the branch of computer science that involves reading text and translating it into a computer understandable digital form (for example, into UNICODE). OCR can be either Offline or Online, depending on the input method. Conversion of scanned image of text data into text document is known as Offline Optical Character Recognition. Online character recognition involves automatic conversion of text the moment it is written on a special digitizer, where a sensor picks up the pen-tip coordinates [X (t), Y (t)] as well as pen-up/pen-down switching and generates signals for computer manipulation [6]. There are various classification methodologies one of the promising methods is Neural Network based Pattern Recognition. This methodology may be Supervised or Unsupervised. Supervised Training is accomplished by presenting a sequence of training vectors, each associated with a target output-vector. In Unsupervised training, a sequence of input vectors is provided, but no target vectors are specified. Selforganizing neural nets classify similar input vectors without the use of training data [7], [9]. We have seen, much of work has been done in recognition of handwritten and machine written English and Arabic characters as in [7], [9],[10]; which not only covers the separate script but also cursive (joined) script. Majority of people in Pakistan and sizeable minorities in India, Bangladesh and UAE, frequently use Urdu language. In addition, Urdu uses characters and writing rules which are common with many other languages like Arabic, Persian, Pushto, Sindhi, etc. A. OCR for Urdu Fonts Urdu Script uses different Fonts for characters of its character set. These characters have different 1 to 4 shapes depending upon the position as given in Table I. Font is a style of writing. More than 10 Fonts are used in Urdu. Some of the most common are Nastaliq, Nasakh, Noori Nasakh, Noori Nastaliq, Koofi, etc. Nastaleeq and Nasakh are the two most popular fonts as discussed below. Nastaleeq is a special calligraphic way of writing Urdu. It does not have a baseline rather the text is centre justified and it is very difficult to recognize this style of writing because of its complexity. Nasakh is another writing style of Urdu which follows one baseline. This way of writing is simpler than Nastaleeq so it is easy to recognize this style because of its simplicity [2]. B. Properties of Urdu Script Direction of Writing: Horizontal-justification of Urdu text is from Right to Left. Cursive Nature: Urdu text is cursive in nature i.e. all the characters are connected to each other within a word. Baseline: Urdu text has a baseline. Baseline is a horizontal line which runs through the text, cutting all the words at same point. Overlapping: Urdu characters some times overlap vertically without touching each other. Diacritics: Diacritics like Hamza, Jazm, Khari Zabar, Khari Zer etc are special symbols which are very important in the proper pronunciation of the word. Ligatures: Ligature is a set of connected characters only. Ligatures collectively make words. In a word, ligatures are separated by one another via half space whereas full space separates one word from other. No Case for characters: Urdu characters have no upper or lower case. Shapes of Context based characters: Shape of the characters depends upon the writing style as well as position in the word e.g. In Nasakh writing style character has 4 different shapes depending upon the position in the word. Strokes: Every Urdu character has one main stroke and zero or more secondary stroke. Some characters have dot(s) with this main stroke. Table I gives a set of Urdu Nasakh characters with 4 different position based shapes /09/$ IEEE 267

2 (Interactive Recognition of Arabic Characters) system for online recognition. This work considers both horizontal as well as vertical scanning of the characters for features. A. Amin [5] concentrates on thinning process and feature extraction; they also discuss the problems encountered in producing satisfactory thinned form of text and describe solutions to those difficulties. These earlier works are very much primitive for ligature and word based recognition for Nastaliq font for Urdu/Arabic characters. Our struggle is a considerable improvement for recognition of Urdu Nasakh font as character-based recognition system. TABLE I. PARTIAL URDU/ARABIC CHARACTER SET II. PREVIOUS WORK Much of research work has been done on OCR systems for different languages. Syed [1] introduce a recognizer for Urdu Noori Nastaliq script. This work is ligature based instead of character based. In this work a multi-tier approach has been utilized. Ligatures are identified in the first step. These ligatures are then classified into special and basic ligatures in Step2. Shah [2] also presented an OCR for Urdu Nastaliq font. It was a ligature based recognizer but use template matching technique for classification. Like Urdu, Arabic is also cursive and Maged [3] used an automatic recognition system of hand written Arabic characters. They used geometrical features to describe the complete skeleton of character. Amin [4] discussed both the Offline as well as online techniques for handwritten Arabic recognition. It discusses the methodology called IRAC III. PROPOSED SYSTEM Our methodology, as described in Fig. 1, assumes only segmented characters and hence it can be used with any system (Ligature/word based recognizer). Initially the system takes the segmented character as image and passes it through some steps that convert it into gray level and then into binary image. System applies some preprocessing techniques as in [10], on binary image and thins the image and makes it font independent so it is more appropriate for the extraction of features. Then another preprocessing technique takes the thinned image and removes the edges which are developed during thinning and may cause features to be invalid. After these preprocessing techniques, system gives the preprocessed image to feature extraction phase [8], during this phase image goes through a number of procedures which find the features of the image. After getting enough features of the image, we applied a weights matrix already fixed, to give results based on clustering. Then classification phase activates its sub phase classification-i, which uses weight matrix of order and gives only one of 33 clusters for 104 segmented characters. As each cluster has more than five characters, therefore the sub phase classification-ii uses additional features to classify each character from other characters in the same cluster. A. System Phases Our proposed system contains four phases as discussed below. 1) Input Phase The image obtained is converted to binary format which is appropriate for the feature extraction, for this, the following steps are carried out. 268

3 If graylevel >= 155 then Make it 1; Else Make it 0; 2) Preprocessing Phase This phase deals with preprocessing techniques discussed below: Thinning: It uses the binary image and makes the image one pixel wide. the obtained thinned image reduces the complexities faced in feature extraction. For thinning, well known Zang Suen algorithm [11] has been used. (unthinned), (thinned) Chain Code Formation: Chain Code formed in the proposed system is 8-directional i.e. system assumes codes associated with eight possible directions as used in [12]. Fig showing 8-Direction Code for pixel c Additional Features Figure 1. Block Diagram of Proposed System Acquisition of image: System takes already saved image of JPEG/JPG format. This image may be RBG/Gray level image. Conversion from RBG to Gray level: This step converts the RBG (colored) format of image into Gray level, the levels are between 0 and 255. Function used for this purpose is grayimg=rgb2gray(rbgimg); takes rbg form of input image and returns into graylevel image. Conversion of Gray level to Binary: This step converts the Gray level image into Binary format which contains only two levels 0 and 1 used for background and foreground respectively. As gray levels are from 0 to 255 so by using 155 as threshold, it is converted into binary values as Chain Code formed for Ra isolated 6,6,6,6,6,6,7,6,7,6,6,7,6,6,6,5,6,6,6,5,6,5,6,5,5,6,5,4,5,5,5,5, 4,5,4,5,4,4,4,4,4,4,4,3. Smoothing: On the basis of Chain Code obtained in previous preprocessing step, proposed system uses one pixel smoothing i.e. if a single value appears in the code sequence then it is converted into value same as the value just preceding it. Following are the images and codes for Ra isolated showing chain code before and after the smoothing process Chain Code before Smoothing: 6,6,6,6,6,6,7,6,7,6,6,7,6,6,6,5,6,6,6,5,6,5,6,5,5,6,5,4,5,5, 5,5,4,5,4,5,4,4,4,4,4,4.. Chain Code after Smoothing: 6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,66,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4. 3) Feature Extraction Phase Features Extracted: Features are very important for recognition. Features are the characteristics of the object. [8]. In this phase different methods are used to extract the 269

4 features with the help of which system recognizes the characters. Height: This feature is set when the character under observation has height greater than width, Width: This feature is set when the character under observation has width greater than height, Loop: Loop is closed path. It is set for all those characters which have closed path e.g. Loop_M: This feature is set for only those characters which contain loop but in the middle of the character e.g., Loop_S: This feature is set for only those characters which contain loop but in the beginning of the character Joint: It is point from which two or three branches exit as in characters., Joint_1: It is selected when character has only one joint Joint_2: It is selected when character has two joints e.g., Joint_3: It is selected when character has three joints Cross: This is set for character which contains crossover, the point from which four different branches exit Curve_R: This feature is selected when the character has curve toward rights side Curve_U: This feature is selected when the character has curve toward up side, Start_H: It is set for characters that start with horizontal line Start_V: It is set for characters that have vertical start End_H: It is set for characters that end with horizontal line End_V: It is set for characters that end with vertical line e.g.,, Endp_1: This feature is selected when the character has only one end point Endp_2: This feature is selected when the character has two end points Endp_3: This feature is selected when the character has 3 end points Endp_4: This feature is selected when the character has 4 end points Figure 2. Graphical User Interface Fig. 2 is a Graphical User Interface of our system that shows the Result YES for the feature Loop in the segmented Character - WAO of End. 4) Recognition Phase a) Classification-I This sub phase uses the core algotithm, Kohonen Self Organizing Maps(K-SOM). K-SOM algorithm [13] is used for initial classification. The algorithm is described as: 1. Initialize parameters and learning rate 2. While stopping condition is FALSE do Step For each input vector x do Step For each j=1 to m i=1 to n 5. Find index j, when D(j) is minimum 6. Update weights for all i with j Wj (new) = Wj (old) + [x Wj (old)] 7. Update learning rate 8. Reduce the radius of topological neighborhood 9. Test the stopping condition The above training K-SOM algorithm uses weight matrix of order and gives only one of 33 clusters for each segmented character. Generally, it takes all the features obtained in the Feature extraction phase. On the basis of 270

5 those features it finds the cluster in which the segmented character being observed fits best. The Clustering is applied on the 54 classes given in Table II. To reach the final set of clusters,we went through a step-by-step process. Initially, we trained the classifier for 30 clusters with a topology of 6 5 grid, the result obtained after Epochs 75 is shown in Table II. This result is not much satisfactory as it makes 23 clusters. Cluster#17 has four characters, - - which are totally distinct characters as far as features are concerned. TABLE III. CLUSTERED CHARACTERS 5) Classification-II As Classification-I only finds the cluster for the character, and each cluster has more than one characters so in addition to basic features some additional features are also required to distinguish a character from others characters of the same cluster. TABLE II. CLUSTERS OF SEGMENTED CHARACTERS We have analyzed results obtained for 100, 150 and 200 epochs using the same topology of grid 6 5 and the results found are slightly different. Changing grid size from 6 5 to 8 7 and repeating the same number (75, 100, 150, 200) of epochs and observed all the results. Likewise, the same number of epochs are applied for the grid 9 8. Finally grid size of was used. After applying epochs 75, 100, 150 and 200 on this grid we observed that the result for the 100 epochs, is quite satisfactory as shown in Table III. This result shows that some of the clusters e.g Cluster no 1,21,27,34,35. contain only one charcter per Cluster. a) Additional Features Dots: This feature is set when the character being processed contains dots,,, Dots_1: It is set when number of dots in the character is only one,, Dots_2: It is set when number of dots in the character is two, Dots_3: It is set when number of dots in the character is three, Dots_A: This feature is set when the dot(s) in the character are above the main region of the character, Dots_B: This feature is set when the dot(s) in the character are below the main region of the character, Dots_M: This feature is set when the dots in the character are in the middle of the character. On the basis of above features, the 104 segmented characters give 54 different classes, as in Table IV. 271

6 VII. FUTURE GOALS Our system recognizes segmented machine written characters of Urdu Nasakh font. Therefore Segmentation can also be included as its one phase. System uses only dots as diacritics so more diacritics can be added in order to achieve more satisfactory results. The work can also be extended easily for handwritten segmented characters. Figure 3. Recognition of TABLE IV. GENERAL CLASSES IV. SYSTEM OUTPUT The output is in the form of fully recognized segmented character which can be used for either making the word or for searching the dictionary or database. V. RESULTS Different Urdu sentences are used to obtain segmented characters. These segmented characters when applied, System successfully achieved recognition rate more than 80%. Fig. 3 shows recognition of (CHE) in software. Fig. 4 shows the result for the cluster for segmented character (PA of Middle). The recognition rate against each segmented character is given below in the Table V. VI. CONCLUSION The proposed system resolves problems of recognition for segmented machine written characters of Urdu Nasakh Font. The system assumes 104 different segmented characters and is fully capable to recognize the segmented characters of any word. The list of all segmented characters is given in the Table I. Classification-I and Classification-II use Kohonen Self Organizing Map (SOM) [9] for recognizing machine written segmented character. Figure 4. Character (PA of Middle) VIII. REFERENCES [1] Syed Afaq Hussain, and Syed Hassan Amin, A Multi-tier Holistic approach for Urdu Nastaliq Recognition, Proceedings of IEEE INMIC Karachi [2] Zahra A. Shah, and Farah Saleem, Ligature Based Optical Character Recognition of Urdu Nastaleeq Font, Proceedings of IEEE INMIC Karachi [3] Maged Mohamed Fahmy, and Maged Mohamed, Automatic Recognition of Handwritten Arabic Characters Using Geometric Features. [4] Adnan Amin, Arabic Character Recognition. Hand Book of Character Recognition And Document. Image Analysis, pp [5] Mandana Kairanifar, and Adnan Amin, Preprocessing and Structural Features Extraction for Multi-Fonts Arbic/Persian, Proc. 5th International Conference on Document Analysis and Recognition ICDAR 99, p.213. [6] 272

7 [7] A. Amin, and S. Singh, Optical Character Recognition: Neural network Analysis of Hand-printed Characters, Proc. 7th International Workshop on Statistical and Structural Pattern Recognition, Sydney, Australia, Lecture Notes in Computer Science 1451, Springer, pp (August 1-13, 1998). [8] John Cowell, and Fiaz Hussain. (UAE) Extracting Features from Arabic Characters. [9] Laurene Fausett, Fundamentals of Neural Networks, Florida Institute of Technology. [10] J. R. Parker, Practical computer Vision using C, John Willey & Sons. [11] [12] H. L. Beus, S. S. H. Tiu, An Improved Corner Detection Algorithm based on Chain Coded Plane Curves, Pattern Recognition, 20: , [13] S. N. Sivanandam, S. Sumathi and S. N. Deepa, Introduction To Neural Networks using MATLA 6.0, Tata McGraw -Hill, TABLE V. RESULT RATE 273