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Handwritten Recognition by Using Machine Learning Approach International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 4, Issue 11, ISSN No. 2455-2143, Pages 564-567 Published Online March 2020 in IJEAST (http://www.ijeast.com) HANDWRITTEN RECOGNITION BY USING MACHINE LEARNING APPROACH P.Thangamariappan Dr.J.C.Miraclin Joyce Pamila M.E., Computer Science and Engineering M.E., PhD, Computer Science and Engineering Government College of Technology Government College of Technology Coimbatore -641 013 Coimbatore -641 013 ABSTRACT: Deep learning is the most powerful used training examples and 10,000 images are used machine learning technique in current testing examples. MNIST database is a combination generation. This paper presents the result of of Special Database 1 and Special Database 3. handwritten recognition using deep learning. Special Database1 consists of digits written by Handwriting is unique to each individual. So the school students. Special Database 3 consists of handwriting is differed from one person to digits written by employees. Half of the training another person. Handwritten Recognition can be and test set have taken from NIST's training dataset, done in two ways. One is Online Handwritten and the remaining training set and test set were recognition and another one is Offline taken from NIST's testing dataset. Handwritten Recognition. Online Handwritten recognition system, which takes the input at run II. THE PROPOSED RECOGNITION time and Offline Handwritten Recognition which SYSTEM works on scanned images. Offline handwritten is the hardest to find the handwriting. MNIST data A. DEEP LEARNING set is used for this handwritten digit recognition Deep Learning is also known as Deep process and it has 70000 handwritten digits. Structured learning, Deep Machine Learning, Many Machine Learning Algorithms are Hierarchical learning. DEEP LEARNING - The developed which can be used for this digit word “DEEP” is refers to the number of layers classification. This paper performs the analysis of through which the data is transformed. Deep accuracies and performance measures of Learning is the combination of machine learning and algorithm Convolutional Neural Networks Artificial Neural Network (ANN). The learning can (CNN). The proposed approach recognizes with be supervised, unsupervised and semi-supervised. overall accuracy is 93%. Deep learning architectures are Deep Neural network(DNN), Deep Belief Network Keywords: Handwritten Recognition, MNIST (DBN), Recurrent neural Network(RNN) Dataset, Deep Learning, Convolutional Neural and Convolutional Neural Networks(CNN) have Networks (CNN). been applied to fields including speech recognition, natural language processing(NLP), I. INTRODUCTION Audio Recognition. Deep learning algorithms are applied to unsupervised learning tasks in machine Handwritten Recognition (HR) is a learning. This is an important benefit because un challenging task in the field of pattern recognition. labeled data are more abundant than the labelled Because every person in this world has their own data. way of writing. In real-time applications like the conversion of handwritten information into digital B. CONVOLUTIONAL NEURAL NETWORK format, verification of signatures, number plate (CNN) recognition, this kind recognition is required. This recognition was implemented using many Machine A convolutional neural network is a Learning techniques like Random Forest, Naive combination of input and an output layer, as well as Bayes and Support Vector Machine, Convolution multiple hidden layers. The hidden layers of a CNN Neural Network(CNN), etc. The accuracy and typically consist of a series of convolutional layers correctness are mostly crucial in handwritten digit that convolve with a dot product. recognition applications. Even 1% error may lead to The activation function is called a RELU inappropriate results in real-time applications. layer. CNN is regularized versions of multilayer The MNIST (Modified National Institute of perceptrons (MLP). Multilayer perceptrons usually Standards and Technology database) data set is used called fully connected networks, that is, each neuron for this recognition process. The MNIST data set has in one layer is connected to all neurons in the next 70,000 handwritten digits. Here 60,000 images are layer. 564 International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 4, Issue 11, ISSN No. 2455-2143, Pages 564-567 Published Online March 2020 in IJEAST (http://www.ijeast.com) to express ideas in fewer lines of code. Python is a programming language that gives you a chance to work rapidly and coordinate frameworks more effectively. A. STRUCTURE OF THE MODEL Analyze the dataset Prepare the dataset III. IMPLEMENTATION Create the model Tensorflow TensorFlow is an amazing data stream in machine learning library made by the Brain Team Compile the model of Google and made open source in the year 2015. It is intended to ease the use and broadly relevant to both numeric and neural system issues in different spaces. TensorFlow is a low- level tool Fit the model for doing entangled math and it targets specialists who recognize what they're doing to construct the exploratory learning structures, to play around and to transform them into running programs. Evaluate the model Mostly, it can be considered as a programming framework in which one can perform the calculations as graphs. Nodes in the graph says B. ANALYSE THE DATASET the math activities and the edges contains the multi-dimensional information clusters (tensors) This paper is used in MNIST (Mixed which is related among them. National Institute of Standards and Technology Anaconda3 5.3.1 database) dataset. This dataset is created by Yaan LeCun, Corinna Cortes, Christopher Burges. The Anaconda is a free and open-source image size is 28x28 pixel square. So the total of 784 appropriation of the Python and R programming pixels. Here the 60000 images are used in training for logical figuring like information science, AI images and 10000 images are used in testing images. applications, large-scale information preparing, Also the MNIST dataset top error rate is 0.21. This prescient investigation, and so forth. Anaconda error rate is achieved by Convolutional Neural accompanies in excess of 1,400 packages just as Network. the Conda package and virtual environment director, called Anaconda Navigator, so it takes C. PREPARE THE DATASET out the need to figure out how to introduce every library freely. Anaconda Navigator is a Graphical Initially loading the MNIST dataset by User Interface (GUI) incorporated into Anaconda using mnist function in Keras. It returns two tuples. appropriation, it enable the clients to dispatch First tuple holding the train data and train label and applications and overview the conda packages, another one tuple holding the test data and test label. conditions, channels without utilize the After loading the dataset, we need to analyze and command- line directions. pre-process the dataset. Reshaping - This is needed step. Because, in Deep Python 3.7 Learning, we provide the original data and label, then that each image has the dimension of [28x28]. Python is broadly utilized universally and is So, we will get 28x28=784 pixel intensities. a high-level programming language. It was Data type - After reshaping, need to change the primarily introduced for prominence on code, and pixel intensities to float32 datatype. As grayscale its language structure enables software engineers image pixel intensities are ranged from 0 to 255. 565 International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 4, Issue 11, ISSN No. 2455-2143, Pages 564-567 Published Online March 2020 in IJEAST (http://www.ijeast.com) Normalize - After declare the datatype we need normalize the floating point values in the range of 0-1. The normalize step is to improve computational efficiency. ONE HOT ENCODING A one hot encoding is used to represent the categorical variables. The categorical variables are represented as binary vectors. First, it requires that the categorical values are mapped to integer values. Then, each integer value is replaced with one and others are zero. Fig.1 Model accuracy and Model loss for test D. CREATE THE MODEL and training dataset. Here using a simple Multi-Layer DATASET VALUE Perceptron (MLP) as our neural network model. Also the neural network model is used in 784 TEST MNIST 88.3 input neurons. Two hidden layers are used. The SCORE DATASET 512 neurons in hidden layer 1 and 256 neurons in TEST MNIST 98.5 hidden layer 2.ReLU is used as the activation ACCURACY DATASET function for the hidden layers and softmax is used Table: 1Comparison of accuracy algorithms as the activation function for the output layer. From the Table we observe the best E. COMPILE THE MODEL accuracy rate of 98.5% is provided by Multi Layer After create the model, we need to Perceptron. compile the model for optimization and learning. We will use multi-label classification problem for V. CONCLUSION the loss function. The result of our work shows that the F. FIT THE MODEL maximum accuracy 98.5% was obtained in MNIST After compile the model, we need to fit dataset using the multilayer perceptron neural the model with the help of MNIST dataset. network technique. A Multilayer Perceptron Neural Network based method is presented here for G. EVALUATE THE MODEL increasing accuracy of offline handwritten text After fit the model, the model can be recognition. We have tested our developed evaluated on the unseen test data. Matplotlib is algorithm. The result is shown below as output used to visualize how our model reacts at image . Future works is focused on further develop different epochs on both training and testing data. the accuracy of recognition by improving pre- processing of data which is fed into deep IV. EXPERIMENTAL RESULT convolution neural network. Fig. 2 Handwritten Digits 566 International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 4, Issue 11, ISSN No. 2455-2143, Pages 564-567 Published Online March 2020 in IJEAST (http://www.ijeast.com) VI. REFERENCES for offline handwritten Chinese character recognition. Pattern Recognition, 72, 72-81.
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