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  1. 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL JOURNAL OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December, 2013, pp. 117-123 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME EVALUATING NEURAL NETWORK AND HIDDEN MARKOV MODEL CLASSIFIERS FOR HANDWRITTEN WORD RECOGNITION Chaitali K.Dhande1, P.M.Mahajan2 1 2 Dept of E&Tc, J.T.Mahajan C.O.E.North Maharasthra Univresity, Faizpur India Dept of E&Tc, J.T.Mahajan C.O.E.North Maharasthra Univresity, Faizpur India ABSTRACT One of the most classical applications of the Neural Network and Hidden Markov Model is the Handwritten Word recognition. This system is base for many different types of applications in various fields, many of which we use in our daily lives. The proposed system would include a simple scheme for two different classifiers .An Neural Networks (NN) classifier based on Multi-Layer Perceptron (MLP) trained with Back Propagation algorithm and Hidden Markov Models (HMM) classifier with two states. HMM classifier is used to identify character in sequence of word with assigning a probability to each of them. NN classifier is used to generate a score for each segmented character. In the end the score from NN and HMM classifier will be compared to get optimized performance. To take the advantages of good properties of both methods, proposed approach will compare the result obtained by NN and HMM Keywords: HMM, NN, MLP, Back Propagation algorithm 1. INTRODUCTION Handwriting word recognition is the ability of a computer to receive and interpret handwritten input from sources such as paper documents, photographs, touch-screens and other devices. According to the way of sensing, Handwriting Word Recognition can be categorizing as Online recognition and Off-line recognition. Off-line handwriting recognition involves the automatic conversion of text in an image into letter codes which are usable within computer and textprocessing applications. In the proposed system off line handwritten word recognition is adapted [3,13]. 1.1 Problem Definition Word recognition can be defined as the categorization of the input character images into identifiable classes via extraction of significant features or attributes of the character from the 117
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME background of irrelevant details. The word recognition system is the process of identifying the sequence of alphabets .The sequence (i.e. word) must be present in database. System should identify all probable combinations of available images and to identify most appropriate word from dictionary. The system refers to two different classifiers: 1. Hidden Markov Model (HMM) Classifier 2. Neural Network (NN) Classifier The system includes a simple scheme for the integration of two different classifiers. NN classifier based on Multilayer Perceptron (MLP) trained with Back Propagation Algorithm and the HMM classifier with two states.HMM classifier is used to identify characters in sequence of word with assigning a probability to each of them. An NN classifier is used to generate a score for each segmented character. In the end the scores from HMM and NN classifier are compared to get optimized performance. An approach is proposed to compare results of NN and HMM, that takes the advantages of good properties of both methods. Finally, the most appropriate word is reordered according to the new composite scores. 1.2 Current State of Arts Handwritten word recognition techniques can be roughly classified into two different approaches: ● Segmentation based approaches: segmentation based approaches have been used widely both for off-line and on-line handwriting recognition. ● Segmentation free approaches: Segmentation free approaches bypass the segmentation phase. In Segmentation based approaches, the word is segmented in various parts and then further processing takes place. Every segment is acting as the one of the element of sequence. Every segmented element can be called as the sub-word unit. Word is ordered sequence of such units. Many systems use HMMs to model sub–word units (characters) and the Viterbi algorithm to find the best match between a sequence of observations and the models [5, 6, 9]. Segmentation based approach is more used in the systems like spelling correction or postal address recognition etc. During the last few years, HMMs have become a very popular approach in handwriting recognition. One of the reasons is their higher performance in medium to large vocabulary applications where segmentation–recognition methods are used. Such methods cope with the difficulties of segmenting words into characters. The Viterbi algorithm is optimal in the sense of maximum likelihood and it looks at the match of the whole sequence of features (observations) before deciding on the most likely state sequence. This is particularly valuable in applications such as handwritten word recognition where an intermediate character may be garbled or lost, but the overall sense of the word may be detectable. On the other hand, the local information is somewhat overlooked. Furthermore, the conditional– independence imposed by the Markov Model (each observation is independent of its neighbors) prevents an HMM from taking full advantage of the correlation that exists among the observations of a single character [10]. 2. RELATED WORK Lawrence [7] describes review of theoretical aspect of HMM as a statistical modeling and show how they are applied to selected problems in machine recognition of speech. In this paper he attempted to present the theory of hidden Markov models from the simple concept of discrete Markov chain. He also attempted to illustrate some applications of the theory of HMMs to simple 118
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME problems in speech recognition and pointed out how the techniques have been applied to more advanced speech recognition problem. Tay etal [12] describes an approach to combine neural network (NN) and Hidden Markov models (HMM) for solving handwritten word recognition problem. To recognize a word, the NN computes the observation probabilities for each letter hypothesis in the segmentation graph. The HMMs then compute the likelihood for each word in the lexicon by summing the probabilities over all possible paths through the graph. They introduce the discriminant training to train the NN to recognize junk. They use three database namely IRONOFF, SRTP and AWS. Also show the superiority of the hybrid recognizer compared to out baseline recognizer, which is using discrete HMM. Finally, they show that the hybrid recognizer can be bootstrapped automatically from the discrete HMM recognizer, and significantly improve its recognition accuracy by going through several training stages. Koerich etal [8,9] they describes a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. An NN classifier is used to generate a score for each segmented character and in the end, the scores from the HMM and the NN classifiers are combined to optimize performance. Experimental results show that for an 80,000– word vocabulary, the hybrid HMM/NN system improves by about 10% the word recognition rate over the HMM system alone. 3. METHODOLOGY The proposed system is Off-line handwriting recognition system which involves the automatic conversion of an image of text into letter codes which are usable within computer and textprocessing applications. 3.1 System Description The system works in two phases, Training phase and Execution phase. In Training phase both the classifiers gets trained with data available and in Execution phase system takes input preprocess it and both classifiers gets executed to recognize the word. 3.2. Training to Neural Network (Multi Layered Perceptron) Classifier The Neural Network is trained by Error Back Propagation Training (EBPT) algorithm in the system. It trains three layers of MLP i.e. input layer, hidden layer and output layer. Note that, first error signal vector is determined at output layer and then it is propagated towards network input nodes. The Error Back Propagation Training algorithm is working as follows [1]. Step 1] Initial weight matrix W and V are initialized at small random values. Step 2] Training starts here. Input is presented and the layers output computed. Step 3] Error value for given input is computed. Step 4] Error signal vector for both , output and hidden, layer is computed Step 5] Output layer weights are adjusted accordingly. Step 6] Hidden Layer weights are adjusted. Step 7] step 2 to step 6 executes predetermined ‘n’ times till maximum error is greater than error value computed in step 3. Step 8] Training Cycle is completed 3.3. Training to Hidden Markov Models (HMM) Classifier HMM plays important role in the system. Many systems uses HMMs to model sub-word units and the Viterbi Algorithm [7,8] to find the best match between the sequence of observation & the models. As HMM is the static model it must needed to frame with finite states. For a word 119
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME recognition system all symbols are considered as states and each word is a particular sequence of the states. For English language there are 26 symbols and every word is combination of some of this symbol. Words may contain one or more alphabets It becomes very rigid in HMM to calculate Initial State Probabilities with more symbols and states. Hence for convenience, in this report, three symbols (A, B, C) and only three letter words are considered. The detailed Block Diagram of Execution phase as shown in fig. Input (Word) Preprocessing Segmentation Feature Extraction MLP classifier Character HMM classifier set Character Sequence probability Combination of results and recognition Fig. 1 Detailed block diagram of system Preprocessing: The input of preprocessing is the image which is a scanned handwritten word. Preprocessing makes the word ready for further processing. Noise Reduction in image processing it is usually necessary to perform a high degree of noise reduction in an image before performing higher-level processing steps. In the presented work removal of noise helps in segmentation and feature extraction. In the system the Gaussian filter is used. Resizing: As for segmentation, in system histogram based algorithm is used, it demands fix size of input image. Hence here the resizing of input image is necessary to bring image in predetermined fix size. For resizing the median is calculated and from it the certain distance is captured. 120
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME Segmentation: In the system, Histogram-based method is used. In the system the all pixel values are summed column wise and depend on the sum values the segmentation is done. The system, segment the word into multiple regions and considers a region at a time to provide as input to HMM classifier. Feature Extraction: When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation set of features In the system every segmented image i.e. a character region pixel values are considered as feature vector for that segment. For all segments such feature vectors can be drawn. All feature vectors are of same size. NN Classifier: Extracted features of individual character are provided to the NN. It provides recognition of each character and provides the set of characters at respective output nodes. HMM Classifier: It also works on the same features extracted of input image. It identify characters by feature and then checks the probability of sequence. If not found in history, HMM suggests nearest sequence possible. 4. RESULTS The system is implemented using MATLAB 7.0 Recognition of word is as follows System provides two end results 1. No.of words guessed by system which has maximum probability w.r.t previous data provided 2. The nearest combination of characters 121
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME No. of Input Word Recognition by HMM Recognition by NN 26 51 77 103 25 49 75 100 20 45 70 90 The results obtained shows comparision of both the methods by taking no.of input words. Efficiency of HMM is better than NN. The system results vary if segmentation phase is not giving proper results. 5. CONCLUSION From Results, we can conclude that the system can be proved as a efficient handwritten word recognition system. An NN classifier is trained to identify each segmented character, so the interpersonal feature variations do not affect the word recognition results. Whereas the HMM classifier is trained for checking the sequence of characters, so wrong sequences can also guess to the proper word. If the HMM is trained for any symbols in any language, it can recognize the proper sequence. But there are some limitations also, main is the rigid structure of HMM and its dependency on the history data. There may be some perfect answer which cannot be guessed by HMM, just because it is not in history or rarely applied in history. NN classifier which do not guess at all. It just generates the answer for what it is trained for. The error limit in EBPT (Error Back Propagation Training) algorithm may vary some results. Both HMM and NN are having very high dependency on the features which used to train them and because of that individual performance of both systems fails in the case of large vocabulary. Combination of both classifiers gives more accurate results even for large vocabulary. Further Works The system can be used in various applications, and for many languages. Only the need is to train the HMM's for those symbols. We can obtain better results if size of history data is increased. The system can be further utilizing in optical word recognition, huge handwritten data processing centers etc. Not only the Word Recognition system, but same HMM logic can be used for the Speech Recognition or pattern identification also. REFERENCES Books [1] [2] Jackel M. Zurada. Introduction to Artificial Neural Systems, Jaico Publishing House, 2004, pg.185-pg.235. Rafael C. Gonzalez and Richard E Woods. Digital Image Processing, second edition, Pearson Education, 2005, pg.589-pg.658. Theses [3] A. Senior. Off–Line Cursive Handwriting Recognition using Recurrent Neural Networks. PhD thesis, University of Cambridge, Cambridge, England, September 1994. 122
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME Proceeding Papers [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] Fu Chang, Chin-Chin Lin and Chun-Jen Chen, Applying a hybrid method to handwritten character recognition Institute of Information Science, Academia Sinica, Taipei, Taiwan M. Y. Chen, A. Kundu, and S. N. Srihari. Variable duration hidden markov model and morphological segmentation for handwritten word recognition. IEEE Transactions on Image Processing, 4(12):1675–1688, 1995. A. El-Yacoubi, M. Gilloux, R. Sabourin, and C. Y. Suen. Unconstrained handwritten word recognition using hidden markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(8):752–760, 1999. Lawrence R. Rabiner, A Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol. 77, no.2, February 1989. A. L. Koerich, R. Sabourin, and C. Y. Suen. Fast two–level viterbi search algorithm for unconstrained handwriting recognition. In Proc. 27th International Conference on Acoustics, Speech, and Signal Processing, Orlando, USA, 2002. To appear. A. L. Koerich, R. Sabourin, C. Y. Suen, and A. El-Yacoubi. A syntax–directed level building algorithm for large vocabulary handwritten word recognition. In Proc. 4th International Workshop on Document Analysis Systems, pages 255–266, Rio de Janeiro, Brasil, 2000. G. Zavaliagkos, Y. Zhao, R. Schwartz, and J. Makhoul. A hybrid segmental neural net/hidden markov model system for continuous speech recognition. IEEE Transactions on Speechand Audio Processing, (1):151–160, 1994. John A. Fitzgerald, Bing Quan Huang, and Tahar Kechadi. An Efficient Hybrid Approach for Online Recognition of Handwritten Symbols. Department of Computer Science University College Dublin Belfield, Dublin 4, Ireland. Yong Haw Tay’, Pierre-Michel hllicad, Marzuki Khalid’, Christian Viard-Gaudin3 Stefan Knerr, Offline Handwritten Word Recognition Using A Hybrid Neural Network And Hidden Markov Model, International Symposium on Signal Processing and its Applications (ISSPA), Kuala Lumpur, Malaysia, 13 - 16 August, 2001. Organized by the Dept. of Microelectronics and Computer Engineering, UTM, Malaysia and Signal Processing Research Centre, QVT. Australia, Yong Haur Tay’, Pierre-Michel Lallican*, Marzuki Khalid’, Stefan Knerr2, Christian WardGaudin3 , An Analytical Handwritten Word Recognition System with Word-level Discriminant Training, 0-7695-1263-1/011$10.00 0 2001 IEEE Yong Haw Tay', Pierre-Michel Lallican2, Marzuki Khalid', Christian Viard-Gaudin3, Stefan Kneer, An Offline Cursive Handwritten Word Recognition System , IEEE Catalogue No. 01 CH37239 0-7803-7101-I/01/$10.000 2 001 IEEE. Gunjan Singh, Avinash Pokhriyal and Sushma Lehri, “Fuzzy Rule Based Classification And Recognition Of Handwritten Hindi Curve Script”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 337 - 357, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Primekumar K.P and Sumam Mary Idicula, “Performance of On-Line Malayalam Handwritten character Recognition using HMM and SFAM”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 115 - 125, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 123