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Handwritten Text Recognition Based on CNN Classification International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 Handwritten Text Recognition Based On CNN Classification Kasturi Madineni, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences. [email protected] Prasanna Vasudevan, Assistant Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences. [email protected] Abstract Handwritten images for characters, digits and special symbols has acknowledged more consideration in research community of pattern recognition due to vast tenders and uncertainty in learning new methods. Primarily, it includes two steps like character recognition and feature extraction, it mainly based on the classification of algorithm. The growth of handwritten charactersareapplicable mainly in cheques, medical prescriptions and tax returns etc., but identifying the handwritten characters are much difficult than the printed characters because each person have different handwriting style. The handwritten recognition mainly based on the convolutional neural network because it provides high accuracy and faster data processing. When compare to capsule neural network and Convolutional neural network the capsule neural network will work faster. The present research working on capsule neural network as classifier, MNIST as dataset with suitable parameters for testing and training for handwritten text recognition. Keywords: Handwritten recognition, convolutional neural network, capsule neural network, MNIST dataset, feature extraction and classification. INTRODUCTION In this paper written characters, digits and symbols have completely different vogue to acknowledge. Neural network is applied in several areas to acknowledge characters and gain additional accuracy. Capsule neural network square measure new sensational in Machine learning and therefore the superior performance on the Kaggle dataset. the favored version of capsule neural network uses associate rule known as “routing by agreement” with completely different layers. The routing rule replaces pooling in CNNs and vector output with scalar. Recognizing characters from written pictures, written text image documents or real pictures is difficult within the domain of optical character recognition (OCR)for tutorial and business applications, the trade of handwriting digit recognition (HDR) is of huge concer.HDR may be a difficult downside that researchers are investigation by victimization machine learning algorithms.HDR is supposed for receiving and deciphering written input within the style of footage or paper documents.However, text extraction from real pictures is in point of fact associate arduous task thanks to vast variations in font size and form, texture and background, etc.Handwriting character recognition is hugely utilized in numerous analysis areas together with process draft, automatic number-plate recognitionand communicating address checking from envelopes and recognition of ID cards ,elementary steps of character recognition (CR) area unit segmentation, feature extraction and classification ,speed progress within the field of character recognition is providing associate proof of the advances in learning algorithmic rule and accessibility of numerous databases together with MNIST,KAGGLE etc., have promotedadvanced analysis within the field of pattern recognition.Among them, MNIST is taken into account to possess benchmark position for endeavor tasks of pattern recognitioncompletely with different classifiers like restrictive Boltzmann machines (RBMs), neural networks (NN) are tested on MNIST dataset.Recently,recognition of written digits victimization CNN as classifier is getting in new analysis zone thanks to numerous applications in deep learning field. To attain higher performances within the domain of character recognition and pattern recognition, deep learning is fast-advancing field among alternative machine learning models thanks to its glorious feature ISSN: 2005-4238 IJAST 6870 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 extraction and dealing as best classifier characteristics. However, the deep neural networking is tried to be time taking network as a consequence of upper quantity of hidden (nonlinear) layers and connections. Currently, a convolutional neural network (CNN) is most approvable tool for image recognition as a result of it uses lesser variety of hidden layers than DNN, comparatively fewparameters. it's terribly simple to coach the system and accustomed extract the position-invariant options during a cheap quantity of your time for its easy structure, ready to map between input dataset to output dataset with temporal subsampling to supply a degree of rotation, distortion and shift invariability. The CNN contains multiple convolution layers with entirely connected convolution (which equals those during a typical artificial neural network).Apicture that is preoccupied as Associate in Nursing input by the CNN passes straight through the convolutional sequences, nonlinear operate, pooling and fully connected layers and finally provides the output. Considering one amongst the foremost difficult drawback within the domain of written digit recognition, heretofore many schemes/algorithms are planned.Since written digits areoften of assorted orientations and designs, researchers face several challenges for automatic recognition of written digits. Ciresanet al. bestowed convolutional neural network committees for written character classification. Arora utilized 2 architectures: feed-forward neural network (FWNN) and convolutional neural network (CNN) for feature extraction, coaching and classification of MNIST dataset constituting written pictures. Outcomes reveal that for the written digit recognition, CNN attains larger accuracy than FWNN. The digit classification accuracy for CNN is 96%, whereas with FWNN is 92%. Ghosh et al. distributed a comparative study of deep neural network (DNN), deep belief network (DBN) and CNN on MNIST dataset. in step with work, the accuracy of classified digits for CNN is > 95% with some error rates. Anil et al.bestowed CNN trained with gradient-based learning and backpropagation algorithmic program for popularity of SouthDravidian characters.Their algorithmicprogram created a most of 80%acc uracy. Shobha Rani reportable work on recognition of 1 of the foremost wide used South Indian script referred to as South Dravidian. The training of character image samples is distributed by victimization one amongst the deep convolution neural networks. Result demonstrates the accuracy of reportable CNN model is 94%. However, the results of former schemes weren't up to the mark in terms of accuracy and process time for written digit recognition method. MNIST dataset used for training and testing The database of NIST subset is compared with MNIST dataset. Out of 70,000 pictures of written digits , 60,000 images are used for training and testing.Determination of each image is 26*26 with element values within the vary of 0–255 (gray scale), zero grey value (in black) is representing background of digit, whereas digit itself is appeared as 255 grey price (in white). The MNIST dataset comprised of tagged coaching and take a look at files, the element values area unit organized in row kind. Therefore, training set file (images) and take a look at set file (images) carries with it 60,000 rows and 784 columns and 10,000 rows and 784 columns, severally. On contrary, within the training and take a look at label files, the labels’ values area unit 0–9. Hence, 10,000 rows and 10 columns for testing files followed by sixty,000 rows and 10 columns (0–9) for coaching label file. Literature work 1. Alejandro Baldominos, Yago Saez, “A Survey of Handwritten Character Recognition with MNIST and EMNIST”, 2019. Alejandro et al has discussed about the MNIST database which contained 70,000 instances, 60,000 used for training and remaining they used for testing. The databases of two different sources: NIST’s Special Database 1 and NIST’s Special Database 3 has been compared. The authors have used these datasets on a convolutional network with two convolutional layers and a dense layer which obtained test error rate ISSN: 2005-4238 IJAST 6871 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 of 0.21%. Highest accuracy was obtained for the Letters database when combined convolutional neural networks with Markov random field models. 2. Poovizhi P, “A Study on Preprocessing Techniques for the Character Recognition”, 2014. The author explained about the importance of the preprocessing steps for the Optical Character Recognition. To implement OCR for any language, preprocessing step is necessary. Steps in preprocessing are: 1) Noise in a document image is due to poorly photocopied pages. 2) Median Filtering, Wiener Filtering method and morphological operations can be performed to remove noise. 3) Median filters are used to replace the intensity of the character image, where as Gaussian filters can be used to smoothing the image. 3. Bartosz Paszkowski, Wojciech Bieniecki and Szymon Grabowski, “Preprocessing for real-time handwritten character recognition”, 2017. Paszkowski et al has discussed the five major phases of the character recognition problem: preprocessing, segmentation, representation,
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