HANDWRITTEN ENGLISH WORD RECOGNITION USING HMM, BAUM-WELCH AND GENETIC ALGORITHM

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 4, July-August 2018, pp , Article ID: IJCET_09_04_019 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication HANDWRITTEN ENGLISH WORD RECOGNITION USING HMM, BAUM-WELCH AND GENETIC ALGORITHM Atma Prakash Singh Department of CSE Maharishi University of Information and Technology, Luck now Ravindra Nath Department of CSE, University Institute of Engineering and Technology, CSJMU, Kanpur Santosh Kumar Department of CSE Maharishi University of Information and Technology, Luck now ABSTRACT We face one problem in in the field of image processing and pattern recognition of computer science, a challenge of correct detection or recognition of the handwritten word. Word recognition talk about the identification of the word which is written by a human being. Most of the researchers are trying to solve recognition problems. We also try to give one solution in this paper to handwritten word recognition, we consider English alphabets of both type and also consider numeric numbers as (A-Z, a-z or 0-9). In this paper we use three approaches Hidden Markov Model (HMM), Baum-Welch and Genetic Algorithm (GA) to identify features of each character and compare with its testing set of characters. we also use stages of handwritten word recognition system that are: read a scanned image of hand written word as HELLO CAN. We take CAN word image, now we converting this CAN word image into binary matrix form (0 and 1), resizing each character of word into the character binary matrix into size of (n x m where n and m may be same), and thinning of an image to get a clear skeleton of each character. Then in this paper identify each character using three algorithms namely: Forward Algorithm, Baum Welch and Genetic Algorithm. The results obtained from each of the algorithm are compared separately and at the end the accuracy of these algorithms are compared separately. Key words: HMM (Hidden Markov Model), GA (Genetic Algorithm), Baum Welch Method (BWM), Handwritten Character Recognition (HC). Cite this Article: Atma Prakash Singh, Ravindra Nath, Santosh Kumar, Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm. International Journal of Computer Engineering and Technology, 9(4), 2018, pp editor@iaeme.com

2 Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm 1. INTRODUCTION A typical handwritten characters recognition system consists of several steps, namely: preprocessing, segmentation, feature extraction, and classification. Several types of decision methods, including statistical methods, neural networks, structural matching (on trees, chains, etc.) and stochastic processing (Markov chains, etc.) have been used along with different types of features. Many recent approaches mix several of these techniques together in order to obtain improved reliability, despite wide variation in handwriting. In this paper, we analyzed the use of Hidden Markov Models (HMMs) for English Language handwritten word recognition. HMMs have been widely used in the field of speech recognition [29] and more recently in handwriting and handwritten character recognition [30] [31]. While most of these word recognition applications concentrate on cursive handwriting and there have been attempts where HMMs were used on individually handwritten characters [32]. We can bifurcate recognition process into two parts: preliminary classification and recognition using HMMs. Firstly, structural properties of the handwritten characters are used to pre-classify an unknown word into a subset of its candidate characters. Then, the nominee characters are added to examined using HMMs. 2. PROCESS OF WORD RECOGNITION Recognition of any thing is comprising an observation with well-defined character /word or image having a meaning. Pattern recognition is inferring a meaning from observation. Designation of pattern recognition system is establishing a mapping from measurement space into the space of potential meanings, whereby the different meanings are represented in this space as discrete target points. The basic modules in pattern recognition are preprocessing, feature extraction and selection, classifier design and optimization Pre-Processing Preprocessing converts the image of a scanned word into a form suitable for subsequent processing. The pre-processing is a series of operations performed on the scanned input image. It usually consists of binarization, normalization, thinning and skeletonizing [15]. Any image processing application suffers from noise like isolated pixels. This noise gives rise to ambiguous features which results in poor recognition rate or accuracy. Hence we take the data set which is noiseless. Binarization process converts a gray scale image into a binary image [16]. After that Thinning is performed to thin the contours of the alphabets in the words. Then skeletonization is used gets the skeleton of character image so that strokes could be conspicuous [2] Flow Chart of Segmentation Process In the segmentation process, an image of English language word is decomposed into subimages of individual English alphabet characters. The following block diagram explains the step of segmentation in the recognition process: editor@iaeme.com

3 Atma Prakash Singh, Ravindra Nath, Santosh Kumar Figure 1 Flow Chart of Segmentation Process 2.3. Implementing the Segmentation Process For splitting the word image into segmented characters, we apply the process of segmentation. Here, we are first dealing with those word images, in which, characters are not linked with each other. These characters are separated by delimiters (spaces) between them. The image of such a scanned input word is shown below: Figure 2 Scanned Input Word Images Above images shows clearly, that the letters are separated by sufficient space between them. Hence we can distinct these individual characters easily. The image of sample word is first binarized and then resized into a 32x32 matrix. Considering the spaces between characters in the original image, we separate these characters by those columns which contain all zeros. Below are the images describing the above process on the English word CAN: editor@iaeme.com

4 Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm Figure 3 Segmentation matrix of word CAN 3. HMM WITH FORWARD ALGORITHM CLASSIFIER In previous chapter we have stated how we can segment an English language word into indivisible English alphabets by considering the example of word CAN. Now taking the same example ahead, we pass the segmented images of indivisible characters C, A, N to the Forward algorithm stated in earlier chapter to calculate the value of P(O λ) (probability of the observation sequence (O) by the models (λ)). We match these P(O λ) value with the range of P(O λ) value of each character which we calculated in earlier chapter using the Forward algorithm and initially developed HMM models. Now, if these values lie in the specified range then we can say that the input character matches with the specified character [3,5]. The range of each character calculated from our HMM Model using the Forward algorithm is given below: Table 1 Range calculated for each English alphabet using Forward algorithm A B C D E F G H I J K L M N editor@iaeme.com

5 Atma Prakash Singh, Ravindra Nath, Santosh Kumar O P Q R S T U V W X Y Z The values of P(O λ) calculated for each segmented character from the input word CAN using the HMM Models and Forward algorithm are given below [18]. Thus from the above result we can conclude that the individual character C matches with character C itself but also with other characters (A, B, I, J, K, X, Y) also. The character A matches with character A itself but also with other Characters (H, I, J, M, W, Y) also and the character N matches with character N itself but also with other Characters (A, H, I, J, M, W, Y) also. Table 2 Results obtained by testing the segmented characters C, A, N C Matching with C A Matching with A N Matching with N 1 A Yes Yes Yes 2 B Yes No No 3 C Yes No No 4 D No No No 5 E No No No 6 F No No No 7 G No No No 8 H No Yes Yes 9 I Yes Yes Yes 10 J Yes Yes Yes 11 K Yes No No 12 L No No No 13 M Yes Yes Yes 14 N No No Yes 15 O No No No 16 P No No No 17 Q No No No 18 R No No No 19 S No No No 20 T No No No 21 U No No No 22 V No No No 23 W No Yes Yes 24 X Yes No No 25 Y Yes Yes Yes 26 Z No No No editor@iaeme.com

6 Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm 4. BAUM-WELCH CLASSIFIER The Baum Welch classifier is used to classify the words of English Alphabets. The word image is segmented into each individual characters (as described in previous chapter) and these segmented characters are classified using Baum-Welch Algorithm. Now the same example of word CAN be taken again, we pass the segmented images of indivisible characters C, A, N to the Baum-Welch Algorithm stated in earlier chapter to calculate the value of P(O λ) (probability of the observation sequence (O) by the models (λ)). We match these P(O λ) value with the range of P(O λ) value of each character which we calculated in earlier chapter using the Baum-Welch Algorithm and initially developed HMM models. Now, if these values lie in the specified range then we can say that the input character matches with the specified character [33]. The range of each character calculated from our HMM Model using the Baum-Welch Algorithm is given below: Table 3 Range calculated for each alphabet using Baum-Welch algorithm 1 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z The values of P(O λ) calculated for each segmented character from the input word CAN using the HMM Models and Baum Welch-Algorithm are given below editor@iaeme.com

7 Atma Prakash Singh, Ravindra Nath, Santosh Kumar Table 4 Result obtained by testing the segmented character C, A, N by using Baum-Welch algorithm C Matching with C A Matching with A N Matching with N 1 A Yes Yes Yes 2 B Yes Yes Yes 3 C Yes Yes Yes 4 D Yes No Yes 5 E Yes Yes No 6 F No Yes Yes 7 G No No No 8 H Yes No Yes 9 I Yes Yes Yes 10 J Yes Yes Yes 11 K Yes Yes Yes 12 L Yes Yes Yes 13 M Yes Yes Yes 14 N Yes Yes Yes 15 O Yes Yes Yes 16 P No Yes Yes 17 Q Yes No Yes 18 R Yes Yes Yes 19 S Yes Yes Yes 20 T Yes Yes Yes 21 U Yes Yes No 22 V Yes Yes No 23 W Yes Yes Yes 24 X Yes Yes Yes 25 Y Yes Yes Yes 26 Z No Yes No Thus from the above result we can conclude that the individual character C matches with character C itself and also with all other characters but except (F, G, P, Z) also. The character A matches with character A itself and also with all other Characters but except (D, G, H, Q) also and the character N matches with character N itself and also with all other Characters but except (E, G, U, V, Z) also. 5. GENETIC ALGORITHM The Genetic Algorithm is used to classify the words of English Alphabets. The word image is segmented into each individual character (as described in previous chapter) and these segmented characters are classified using Baum-Welch Algorithm. Now the same example of word CAN be taken again, we pass the segmented images of indivisible characters C, A, N to the Genetic Algorithm stated in earlier chapter to calculate the value of P(O λ) (probability of the observation sequence (O) by the models (λ)). We match these P(O λ) value with the range of P(O λ) value of each character which we calculated in earlier chapter using the Genetic Algorithm and initially developed HMM models [34]. Now, if these values lie in the specified range then we can say that the input character matches with the specified character. The range of each character calculated from our HMM Model using the Genetic Algorithm is given below [1,5,6]: editor@iaeme.com

8 Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm Table 5 Range calculated for each alphabet using Genetic algorithm 1 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z The values of P(O λ) calculated for each segmented character from the input word CAN using the HMM Models and Genetic-Algorithm are given below. Table 6 Result obtained by testing the segmented character C, A, N using Genetic algorithm C Matching with C A Matching with A N Matching with N 1 A Yes Yes Yes 2 B Yes Yes Yes 3 C Yes Yes Yes 4 D No No No 5 E No No No 6 F Yes No Yes 7 G Yes No No 8 H No Yes No 9 I Yes Yes Yes 10 J Yes Yes Yes 11 K Yes No Yes 12 L No Yes No 13 M Yes Yes Yes 14 N Yes Yes Yes 15 O No No No 16 P Yes No No 17 Q No No No 18 R Yes Yes Yes editor@iaeme.com

9 Atma Prakash Singh, Ravindra Nath, Santosh Kumar 19 S No No No 20 T No No No 21 U Yes Yes Yes 22 V Yes No No 23 W Yes Yes Yes 24 X No No No 25 Y Yes Yes Yes 26 Z No No No Thus from the above result we can conclude that the individual character C matches with character C itself and also with all other characters but except (D, E, H, L, O, S, T, X) also. The character A matches with character A itself and also with all other Characters but except (D, E, F, G, K, O, P, Q, S, T, V, X) also and the character N matches with character N itself and also with all other Characters but except (D, E, G, H, L, O, P, Q, S, T, V, X, Z) also. 6. RESULT ANALYSIS AND CONCLUSION The word image is segmented into each individual character and these individual characters are classified by using three techniques namely: Forward Algorithm, Baum-Welch Algorithm, and Genetic Algorithm. The same word image CAN be segmented into individual character C, A, N. These individual s characters are passes to our HMM Model and the above mentioned algorithms (Forward Algorithm, Baum-Welch Algorithm, and Genetic Algorithm) are applied on them. The table listed below shows the efficiency of the all the three algorithms for the English alphabet C, A, N, as below in table: Table 7 Comparison of various classification algorithms used in the project Algorithm Efficiency for recognizing English Alphabet A Forward Algorithm % (approx..) Baum-Welch Algorithm % Genetic Algorithm % REFERENCE [1] Zhou Kenong, "Genetic Algorithm Efficient Implementation", Control Theory and Application, vol. 19, no. 5, pp , 2002 [2] Rajib Lochan Das, Binod Kumar Prasad, Goutam Sanyal English Character Recognition using Global and Local Feature Extraction, International Journal of Computer Applications ( ) Volume 46 No.10, May [3] El-Yacoubi,R. Sabourin, M. GillouxC.Y. Suen Off- Line Handwritten Word Recognition using Hidden Markov Model. [4] Binod Kumar Prasad, Goutam Sanyal, Department of Computer Science and Engineering, National Institute of Technology, Durgapur, INDIA, A Model Approach to Offline English Character Recognition International Journal of Scientific and Research Publications, Volume 2, Issue 6, June ISSN [5] Zhang Shan asked, Textbooks. MATLAB genetic algorithm toolbox and its application, Xi'an:Xidian University Press, pp , [6] Bottaci, L.,2001, A Genetic Algorithm Fitness Function for mutation testing presented at SEMINAL 2001, International workshop on software engineering using Meta heuristic editor@iaeme.com

10 Handwritten English Word Recognition Using HMM, Baum-Welch and Genetic Algorithm Innovative algorithm, a workshop at 23-rd Int. Conference on Software Engineering, Toronto, [7] Lawrence R. Rabiner, IEEE, A Tutorial on Hidden Markov Models and selected applications in Speech Recognition. [8] D. Lee, S. W. Lam and S. N. Srihari, "A structural approach to recognize hand-printed and degraded machine-printed characters," submitted to the Symposium on Syntactic and Structural Pattern Recognition, Murray Hill, New Jersey, [9] J. J. Hull, "Inter-word constraints in visual word recognition," Proceedings of the Conference of the Canadian Society for Computational Studies of Intelligence, Montreal, Canada, May 2123, 1986, 134- [10] Nafiz Arica and Fatos T. Yarman-Vural [11] J. Serra, Morphological filtering: An overview, Signal Process., vol. 38, no. 1, pp. 3 11, [12] M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, 2nd ed. Pacific Grove, CA: Brooks/Cole, [13] H. S. Baird, Document image defect models, in Proc. Int. Workshop Syntactical Structural Pattern Recognit., 1990, pp [14] M. Cannon, J. Hockberg, and P. Kelly, Quality assessment and restoration of typewritten document images, Int. J. Document Anal. Recognit., vol. 2, no. 2/3, pp , [15] C. Downtown and C. G. Leedham, Preprocessing and presorting of envelope images for automatic sorting using OCR, Pattern Recognit., vol. 23, no. 3 4, pp , [16] W. Guerfaii and R. Plamondon, Normalizing and restoring on-line handwriting, Pattern Recognit., vol. 26, no. 3, pp , [17] L. O Gorman, The document spectrum for page layout analysis, IEEETrans. Pattern Anal. Machine Intell., vol. 15, pp , [18] R. G. Casey and E. Lecolinet, A survey of methods and strategies in character segmentation, IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp , July [19] A. K. Jain, R. P. W. Duin, and J. Mao, Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Machine Intell, vol. 22, pp. 4 38, Jan [20] H. D. Block, B. W. Knight, and F. Rosenblatt, Analysis of a four-layer serious coupled perception, Rev. Mod. Phys., vol. 34, pp , [21] A. K. Jain and D. Zongker, Representation and recognition of handwritten digits using deformable templates, IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp , Dec [22] M. Bokser, Omnifont technologies, Proc. IEEE, vol. 80, pp , [23] Dewi Nasien, Habibollah Haron, Siti Sophiayati Yuhaniz. [24] Suliman, A. Shakil, A. Sulaiman, M. N. Othman, M. and Wirza, R. Hybrid of HMM and fuzzy logic for handwritten character recognition. In Proceedings of Information Technology. Kuala Lumpur, Malaysia, 2008 [25] Zhaoqi, B. and Xuegong, Z. Pattern Recognition, 2nd Edition, Tsinghua University Press., (2000) [26] Ravindra Nath, and Renu Jain Using Randomized Search Algorithms to Estimate HMM Learning Parameters IEEE International Advanced Computing Conference (IACC-2009) editor@iaeme.com

11 Atma Prakash Singh, Ravindra Nath, Santosh Kumar [27] A.H. Mantawy, L. Abdul Mazid, Z. Selim Integrating genetic Algorithms, Tabu search and Simulated Annealing for the unit commitment problem, IEEE Transaction on power system, Vol.14, No. 3 August [28] Mark Pirlot, general local search method, European journal of operational research -92 (1996) [29] L. R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, vol. 77, no. 2, 1989, pp [30] H. Bunke, M. Roth and E. Talamazzini, Off-line Cursive Handwritten Recognition using Hidden Markov Models, Pattern Recognition, vol. 28, no. 9, 1995, pp [31] H.J. Kim, K.H. Kim, S.K. Kim and J.K. Lee, Online Recognition of Handwritten Chinese Characters based on Hidden Markov Models, Pattern Recognition, vol. 30, no. 9, 1997, pp [32] G. Loudon, C. Hong, Y. Wu and R. Zitserman, The Recognition of Handwritten Chinese Characters from Paper Records, IEEE TENCON, Digital Signal Processing Applications, 1996, pp [33] Handwritten English Character Recognition using HMM, Baum-Welch and Genetic Algorithm. Ravindra Nath et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 7 (4), 2016, [34] Mark Pirlot, general local search method, European journal of operational research -92 (1996) editor@iaeme.com