Perfo rmance Analysis of Network Algorithms in Character Recognition In this paper, scanned answer script is given as input image which is to be teste

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1 I nternational Performance Analysis of Network Algorithms C haracter Recognition in Y amuna U, Shreelakshmi C S Abstract- Character recognition is one of challenging tasks in Image Processing and Artificial Networks r esearch field. It has variety of applications such as banking, robotics, security products and or research fields etc. This paper is aimed to r ecognize numbers using neural network technology. Character recognition process mainly consists of four stages such as image a cquisition, pre processing operations, i nitialization, creation of neural network and recognition. Characters are r ecognized using a scanned document or acquisition of image directly using MATLAB. network algorithm helps in character recognition based on f eatures extracted from database. In this paper, performance of different neural network algorithms such as gradient descent back propagation, gradient descent with adaptive learning rate back propagation, gradient d escent with momentum and adaptive learning rate back propagation are a nalyzed a nd compared. This approach is implemented in MATLAB using image processing f unctions [ 1]. Keywords: Character recognition, p rocessing, Networks I. IN TRODUCTION Gradient algorithms, Image C haracter recognition plays a very important role in communication field. As growth of internet increases, whole world is going to be d igitized, and also demand of online information system is increased. Character recognition s ystem find applications in bank cheque transaction, number p late recognition, and writer identification [ 2]. Character recognition is mainly partitioned into two types handwritten and typewritten. In type written character recognition, system uses documents which has already typed and scanned. For e xample, documents in libraries, offices and companies. In handwritten character recognition, system tries to identify character which has written by human. Handwritten character recognition is mainly divided into two types ie., offline and o nline character recognition, where offline gives good accurate results. This paper mainly concentrates on Handwritten number recognition. It is one of challenging tasks and it is more complex to interpret because it is different i n styles, sizes and orientation also varies from one person to p erson [ 3], [4], [5]. Manuscript received Nov 22, 2016 Y AMUNA U, Digital Electronics and Communication Systems, Malnad C ollege Of Engineering SHREELAKSHMI C S, Digital Communication and Networking, G SSSIETW, Mysore, India Here, neural network technology is used to r ecognize characters. A neural network is explained as a network which is created to perform desired task in away like human brain do w ork. Human brain as developed its own rules to identify particular pattern since birth time, it is often called as an experience. Like human brain, neural network stores knowledge regarding pattern through learning process. Algorithm is required to train system and make it to learn c haracters which are written in different styles. I nitializing network weights and biases for network training n it can be t rained for pattern association, classification and function a pplication. Training process mainly requires network inputs a nd target outputs [ 6], [7]. Several training algorithms used f or feed f orward networks. T his algorithm used gradient of performance function means it determines how to a djust weights to m inimize p erformances. G radient uses t echnique called back propagation. It is mainly used because to update network weights and biases gradually p erformance function also decreases rapidly [ 8]. I I. M ETHODOLOGY This paper mainly uses four methods such as Image acquisition, Image pre- processing operations, I nitialization a nd creation of neural network, handwritten number recognition. Here, we have applied four different neural network algorithms for character recognition and ir performances are compared. Flow of this method is as shown b elow. F ig.1. Flow chart for proposed m ethodology A. I MAGE ACQUISITION: 54

2 Perfo rmance Analysis of Network Algorithms in Character Recognition In this paper, scanned answer script is given as input image which is to be tested. It contains question numbers in margin. Our main task is to recognize question n umbers which are written in different styles. F ig.3.gray conversion of input image F ig.2. I nput image B. IMAGE PRE- P ROCESSING OPERATIONS: In pre- processing step, scanned answer sheet is converted into gray n to binary to obtain accurate results. Image contains some noise. To remove noise from image, morphological o perations are used. Morphological operations are defined as process of defining shape and form of objects. Morphological S structuring element and create an output image of same size. Basic morphological operations are dilation and erosion. H ere, we have used dilation and open operations. D ilation is used to make objects larger and to increase thickness of image. strl is function used to construct structuring o bjects. Open c onsiders only thick connections and removes n oisy portions and smoon i mage. Next step is number extraction. To extract each question number in scanned answer script, firstly margin is cropped, n each number is cropped to edge. Cropping is done by using starting and e nding points of rows and columns of each number. Each question number is scaled down to 10*10 single vector c haracter representations. F ig.4.binary conversion of i nput image F ig.5. Noise free cropped margin in i mage 55

3 I nternational C.INITIALIZATION AND CREATION OF NEURAL N ETWORK: Network is created using initial parameters such as goal, epochs, performance, learning rate, and momentum. N eural network is initialised and trained using neural network algorithms and database. Database contains numbers which are written in different styles. Features of all numbers which are present in database are extracted a nd used by algorithms to recognize numbers. Based on statistical, structural approaches, features of all numbers a re calculated [ 5], [6]. D.HANDWRITTEN NUMBER RECOGNITION: O nce feature extraction is completed, number with maximum matching i s displayed. Fig.8. Regression plot shows accuracy in traingd 2. G radient descent with momentum (traingdm): T his algorithm acts like low p ass filter. This momentum allows network to ignore small features in error surface. Training parameters of se a lgorithms are epochs, goal, time, minimum gradient, maximum fail, learning rate a nd momentum. H ere, learning rate and momentum is constant that means 0 for no momentum and 1 for insensitive t o local gradient ant it does n ot learn properly. Both traingd a nd t raingdm are too slow for practical problems. F ig.6. Recognised Numbers d isplayed in command w indow E.NEURAL NETWORK ALGORITHMS: H ere, four neural network algorithms are a nalyzed, compared a nd explained. 1. G radient descent back propagation (traingd): T his algorithm comes under batch mode. In this algorithm batch mode weights and biases of network are updated after training set is applied to network. The batch steepest d escent function is traingd. Training parameters of this a lgorithm a re epochs, goal, time, minimum gradient, m aximum fail, learning rate. I f step is bigger, n learning rate is larger. If learning rate is too large n algorithm becomes unstable and also it takes more time to c onverge. Fig.9. Ne ural network training tool using traingdm Fig.7. network training tool using train gd Fig.10. Regression plot shows accuracy in traingdm 56

4 Perfo rmance Analysis of Network Algorithms in Character Recognition 3. gradient descent with adaptive learning rate back p ropagation (traingda) and gradient descent with momentum and adaptive learning rate back p ropagation(traingdx) In se algorithms learning rate is constant throughout t raining. The performance of algorithms is very sensitive to t he proper setting of learning rate. I f learning is set too h igh, t he algorithm can oscillate and become unstable. If learning rate is set too small n it takes long time to c onverge. Adaptive learning rate keep learning step size a s large as possible while keeping learning stable. Here new e rror is less than previous error as learning rate is i ncreased. This procedure increases learning rate but only to e xtent that network can learn without large error i ncreases. L arger learning rate results in stable learning. L earning i s high it guarantees t o decreases in error. It d ecreases until stable learning resumes. T he function traingdx c ombines adaptive learning rate with momentum. Fig.13. network training tool using traingda Fig.11. network training tool using traingdx Fig.14. Regression plot shows accuracy in traingda T able 1: Performance analysis of neural n/w algorithms Fig.12. Regression plot shows accuracy in traingdx C ONCLUSION I n this paper, we have discussed and a nalyzed different neural n etwork algorithms for han dwritten character recognition. H ere, four neural network algorithms are a nalyzed and c ompared. A ll algorithms give accuracy of 98% but traingd a nd traingdm are too slow to get solution for real time a pplications because it takes more iterations. So t raingda and t raingdx give better performance compare to previous a lgorithms. 57

5 R EFERENCES I nternational [ 1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd Ed.. [ 2] J. Pradeep,E.Srinivasan,and S Himavathi, network based h andwritten character recognitio n system without feature extraction International Conference on Computer, Communication and Electrical Technology (ICCCET), pp.40-44,2011. [ 3] M.F. Kader,M.K.Hossen, Asaduzzaman and A.S.M Kayes, An Offline H andwritten Signature V erification System as a Knowledge Base, Computer Science and E ngineering Research Journal, C SE,CUET, Vol.04,2006 [ 4]. R. Plamondon and S.N. Srihari., 2000, Online and off- line handwriting r ecognition: a comprehensive survey., Pattern Analysis and Machine Intelligence, IEEE Transac- t ions on, vol. 22(1), pp [ 5]. R. Plamondon and S. N. Srihari, ''On-line and off- line handwritten c haracter recognition: A comprehensive survey'', IEEE Transactions on PAMI, Vol. 22 (1), pp , [ 6] Žiga Zadnik Handwritten character Recognition: Training a Simple NN f or classification using MATLAB [ 7] Mathias Wellner, Jessica Luan, Caleb Sylvester, Recognition of H andwritten digits using a Network, 2002 [ 8] Ankit Sharma, Dipti R Chaudhary, Character Recognition Using Network International Journal of Engineering Trends and Technology ( IJETT) - Volume4Issue4- A pril Y amuna U completed M. Tech in Digital Electronics and Communication Systems from Malnad college of Engineeri n g, Hassan. She did her B.E degree in Electronics and Communication Engineering from Malnad college of Engineering, Hassan, Karnataka, India. Her area of interest is Image Processing, n etworks and Embedded systems. S hreelakshmi C S c ompleted M. Tech in Digital Communication and Networking, GSSSIETW, Mysore, I ndia. She did her B.E degree in Instrumentation Technology from Malnad college of Engineering, Hassan, Karnataka, India. Her area of interest is W ireless communication and networking, Image p rocessing and networks. 58