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, recognition, and post-processing. They developed preprocessing algorithms which help to achieve high accuracy rate without a visible delay in recognition process.

4. Gaurav Kumar, Pradeep Kumar Bhatia, “Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition”, 2013.

Gaurav Kumar et al has dealt with the various preprocessing techniques involved in character recognition system with different kind of images ranging from simple handwritten form based documents and documents containing colored and complex background and varied intensities. The preprocessing techniques like skew detection and correction, image enhancement techniques of contrast stretching, binarization, noise removal techniques, normalization and segmentation, morphological processing techniques were discussed.

5. Ranyang Li, Hang Wang, Kaifan Ji, “Feature Extraction and Identification of Handwritten Characters”, 2015.

The authors explained about the feature extraction and classification recognition methods of off-line handwritten characters recognition. The feature extraction methods mainly include two classes: statistic features and structure features, and the classification methods covered were pattern matching, statistical classifier, neural network classifier, etc. These techniques were analyzed and compared for their performance on handwritten character recognition.

6. Manju Rani Yogesh Kumar Meena, “An Efficient Feature Extraction Method for Handwritten Character Recognition”, 2016.

The author described about three main steps of handwritten character recognition- Data collection and preprocessing, feature extraction and classification. Data collection includes creating a raw file of handwritten character images. Preprocessing steps used to find a normalized binary image of handwritten character. Feature extraction is the process of gathering data from different samples. The authors concluded that the recognition rate has improved when the preprocessing technique such as skeletonization and normalization were applied on binary images of characters.

7. Amandeep Kaur , Seema Bagla, “Study of various characters segmentation techniques for handwritten offline cursive words”, 2015.

Amandeep Kaur et al have presented as detailed study on the character segmentation techniques. In their study, they have shown that handwritten characters when used as image can be decomposed into the sub-images. The segmentation can be done on the various parts of the text such as, a line segment of text, a word segment from line and character segment from word. The segmentation can be applied either horizontally or vertically, which separates the different logical parts, like text from graphics, line of a paragraph, and characters of a word.

8. Megha Agarwal, Shalika, Vinam Tomar, Priyanka Gupta, “Handwritten Character Recognition using Neural Network and Tensor Flow”, 2019.

The analysts proposed the utilization of hybrid or half plus half concealed markov show (HMM) to perceive the handwritten content in disconnected mode. The optical model's basic part was prepared with markov chain procedure and a multilayer perceptron likewise used to gauge the probabilities.

9. Mustafa S. Kadhm, Alia Karim Abdul Hassan, “Handwriting Word Recognition Based on SVM Classifier”, 2015.

Handwritten word recognition system based on Support Vector Machine Classifier was the work proposed by the authors. Their work mainly focuses on the word level of handwritten text. An Arabic handwriting dataset AHDB, dataset was used by them for training and testing their system. The system has used several feature extraction methods and the resultant data was given to SVM classifier. The authors claimed that their method has achieved the best recognition accuracy of 96.31%. Experimental results have also shown that the polynomial kernel of SVM is convergent and produced more accurate results for recognition than other SVM kernels.

10. Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz, “Off-Line Handwritten Character Recognition System Using Support Vector Machine”, 2017.

SVM is one of the classifier used by the author for the recognition of Indian and Arabic handwritten numeral characters. The ability of MLPs to recognize similar character has been improved by specialized local SVM. SVM is applied to multiclass character recognition problem using one versus all method. C- SVM as the classifier and polynomial function as the kernel type have been used. The SVM is trained with the training samples from CEDAR dataset.

The authors have claimed that the method proposed by them has outperformed for handwritten alphabets when compared to the most state of the art methods examined.

11. S. S. Manikandasaran, “Recognition of English Handwritten Characters”, 2013.

The author mainly described about the Max pooling method. Max pooling is the process of sampling of the activation feature sets. Normally, Max pooling layers of 2x2 filter and stride 2 are used, which helps in reducing the input activation maps into half spatial maps. Max pooling is a sample-based separation process into their particular categories, which help in representing the input – image into a Matrix, thus by providing a well-defined and required form of feature set through over-fitting model. It optimizes the computational cost by reducing the number of attributes to train and produces simple translation invariance to the internal representation. This process is done by applying a max ﬁlter to the non-overlapping subregions of the initial representation.

12. S. Ramgovind, Mm Eloff, E.Smith, “Online Grammar-Based Recognition of Handwritten Sentences”, 2012.

The author represents about Flattening. Flattening transforms the tridimensional image dimensions into a non dimensional image. The two-dimensional convolution layers making a two-dimensional dataset such as images etc. usually output a tridimensional image with the dimensions being the image resolution by removing the filters, additional convolutional layers and the number of unwanted patterns. This structure is required to convolution layers together or with other layers that provide a treatment such as pooling, upscaling, etc. Within classiﬁcation, usage of fully connected layers that do not take any structure into account for processing are done in the last steps of the network. The output of the last convolution layers were considered as a large piece of unstructured data.

13. M. M. A. Ghosh and A. Y. Maghari, “A Comparative Study on Handwriting Digit Recognition Using Neural Networks”, 2018.

The authors describe about the prediction of the results based on the provided training datasets and the testing models. The final step is predicting the categories of new images. The testing model provides various separate images for testing purposes. The model classified multiple images at a time, all images inside a directory and the subdirectories are classified one by one. The prediction was a single label or all labels with the likeness of the image belonging to that label. The accuracy appeared to be high in training set. The problem of over fitting was also avoided and the test accuracy improved. The accuracy was further improved by training the model with more data and the model was also deeper with more number of layers.

14. RajkumarBuyya, Chee Shin Yeo and Srikumar Venugopal, “Handwritten Recognition using SVM Classifier”, 2014.

The CNNs was used by Rajkumar et al to extract the unique properties of the image. It was found that CNN was capable of processing large image datasets in minimum time and thus CNNs were helpful in the classification of different datasets into their respective classes. The working of the Convolutional layer was also described in detail as follows - the input layer received input as an image with a specific dimension of different height, width and depth. Filters were designed as matrices and initialized with random numbers. The Filter was designed over input volume and computes dot product throughout the image. The Filters end up producing activation maps for the input image. Finally the preprocessed images were used for training the SVM classifier and tested for accuracy. It was found that the procedure used by the authors has produced better accuracy in recognition of handwritten text.

15. Michael Armbrust Armando, Fox Rean Griffith Anthony, “An efficient and improved scheme for handwritten digit recognition based on convolutional neural network”, 2015. Character recognition from handwritten images has lack of high accuracy and low computational speed for handwritten digit recognition process. The author proposed endeavor to make the path toward digitalization clearer by providing high accuracy and faster computational methods for recognizing the handwritten digits. The convolutional neural network as classifier, MNIST as dataset, with suitable parameters for training and testing, and DL4J framework for hand written digit recognition were used by the authors. This imparted accuracy up to 99.21% which is higher than formerly proposed schemes. In addition, the proposed system reduces computational time significantly for training and testing due to which algorithm becomes efficient.

16. H.I. Avi-Itzhak, T.A. Dliep , H. Garland, “High accuracy optical character recognition using neural network”, 2018. Avi-Itzhak et al used Convolutional Neural Network and have claimed that it has produced better accuracy, but it was a linear model where only the last layer will only be directly considered for

ISSN: 2005-4238 IJAST 6874 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 classification. So in order to use the low, middle and high level features for classification they used multi- scaled CNN called Directed Acyclic Graph Convolutional Neural Network.

17. Anita Pal , Dayashankar Singh, “Handwritten English Character Recognition Using Neural Network”, 2015.

Handwritten English Character using a multilayer perceptron with one hidden layer was the method implemented by the authors. The features were extracted from the handwritten character by boundary tracing. Characters were identified by analyzing its shape and comparing its features that distinguishes each character. Also an analysis was carried out to determine the number of hidden layer nodes to achieve high performance of backpropagation network in the recognition of handwritten English characters. The system was trained using 500 samples of handwritings given by both male and female participants of different age groups. Test result was performed on 500 samples other than samples that were used for training. They claim that Fourier Description combined with backpropagation network provided good recognition accuracy of 94% for handwritten English characters with less training time.

18. Neves, “Off-line handwritten digit recognizer”, 2016.

Support Vector Machine were used by the authors to recognize offline handwritten characters and have proved that it produced better accuracy for standard dataset NIST SD19. In spite of the fact, MLP was best classifier for nonlinear classes (separable) segmentation.

19. Ghosh and Maghari, “A comparative study on handwriting digit recognition using neural networks”, 2016.

The author described about three neural network approaches demonstrating that DNN was the best algorithm with 98.08% accuracy. However, every neural network has some error rate due to similarity in digit shape (e.g., 3 and 8 and 6 and 9). After deep analysis, they found that CNN was better classifier than support vector machine (SVM), K-nearest neighbor (KNN) and random forest classifier (RFC) for HDR.

20. Bodhisatwa Manda and Suvam Dubey “Handwritten Indic Character Recognition Capsule Networks”, 2019.

The initial efforts have used Gabor wavelets and moments functions for the character recognition. With the introduction of Machine Learning, SVMs and feature vectors have been tried to obtain acceptable accuracies. Deep Belief Networks, ANNs have also been used claiming a considerable increase in results. Further advanced techniques such as CNN have been reported to be used to recognize Kannada numerals only. In the work proposed by Manda et al, Capsule Networks has been found performing better compared to all the state of the art technology in the field of Computer Vision.

21. Ramesh. G, J. Manoj Balaji, Ganesh “Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network”, 2019.

Ramesh et al identified and classified handwritten character or texts of various different languages. The source of input was from various documents, photographs and other surface devices which enable characters to be written on them. Effort towards advances in captured scene text detection and recognition have been made to identify state-of-the-art algorithms and predicted possible research directions.

22. Raphaela Heil, Ekta Vats and Anders Hast “Exploring the Applicability of Capsule Networks for Word Spotting in Historical Handwritten Manuscripts”, 2016. The author described the impact of the amount of training data on the recognition performance, the model and embedding strategy that perform best on the aforementioned data splitting will be fully retrained with varying amounts of data (e.g. one-to-n instances per word). Lastly, for all of the experiment setups

ISSN: 2005-4238 IJAST 6875 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 described above, the work was intended to examine the time that is required to train each of the networks, as a basis for further comparisons with state-of-the-art approaches.

23. Rami Aly, Steffen Remus, and Chris Bieman “Hierarchical Multi-label Classification of Text with Capsule Networks”, 2015. The author described about capsule networks to the HMC task indicated that the beneficial properties of capsules can be successfully utilized. By associating each category in the hierarchy with a separate capsule, and using a routing algorithm to combine the capsules encoded features, capsule networks identified text with similar features more accurately than the baselines. A real world dataset, the BlurbGenreCollection (BGC), is compiled with the promising properties of capsule networks for HMC task. Most hierarchically organized datasets consist of substantial amounts of rare label combinations, where algorithms were very likely to be confronted with unseen label combinations.

Existed system

A special quite artificial neural network includes of input layer, output layer and multiple hidden layers called CNN. Hidden layer constitutes network of repetitive convolutional and pooling layers therefore finally ends at one or additional absolutely connected layer.

Proposed System

The primary concern with CNNs square measure that the various kernels work severally. If 2 kernels square is measure trained to activate for two specific elements of associate object, they will generate a similar amount activation regardless of the relative position of the object. Capsule networks bring an element of agreement between kernels within the equation. Consequent layers receive higher activations once kernels such as completely different elements of the object accept as true with the final agreement. The capsule network proposed with different capsule layers. A Primary capsule layer that teamsconvolutions together as a capsule unit.

Convolutional layer (CNL)

CNL is that the first layer in CNN which memorizes the features of input image covering its entire region during scanning through vertical and horizontal sliding filters. It adds a bias for each region followed by evaluation of real of both filter values and image regions. For thresholding element-wise activation function, like max(0, x), sigmoid and tanh, is applied to output of this layer via rectified long measure.

Pooling layer (PL)

At second, there comes pooling layer which is additionally called as max pooling layer or subsampling. In pooling layer (PL), shrinkage within the volume of information takes place for the simpler and faster network computation. Max pooling and average pooling are main tools for implementing pooling. This layer obtains maximum value or average value for every region of the input file by applying vertical and horizontal sliding filters through input image and reduces the amount of information.

Fully connected layer or dense layer

Lastly, there's fully connected layer after convolution and pooling layer within the standard neural network (separate neuron for every pixel) which is comprised of n numbers of neurons, where n is that the predicted class number. for instance, there are ten neurons for ten classes (0–9) in digit character classification problem. However, there should be 26 neurons for 26 classes (a–z) for English character classification problem.

RESULTS:

Conclusion

In our existing work we've got enforced the capsule networks on written Indic language digits and character databases. We have shown that capsule networks area unit a lot of more and strong compared to the LeNet design. we've got conjointly seen that capsule networks will act as a supporter once shared with alternative networks like LeNet and AlexNet. The most effective concert was achieved by combining AlexNet with capsule networks for many of the datasets. Just in case of Telugu dataset, combination of all 3 networks worked the most effective. From the results it will be complete that even with seven timesmore parameters that capsule networks the AlexNet unsuccessful to capture some information from the capsule network learnt. Thus, it absolutely was ready to improve the performance of Alexnet. Finally, its conjointly been seen the capsule network converge much quicker thatLeNet. In terms of pros and cons use of capsule network will be useful for learning with a lot of lesser range of options and

ISSN: 2005-4238 IJAST 6878 Copyright ⓒ 2020 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 9s, (2020), pp. 6870-6880 conjointly as improvement technique for alternative larger networks, the matter with capsule network is slow unvarying method and limitation to single layer routing.That reveals several avenues of analysis.

FUTURE SCOPE

New features can be added to improve the accuracy of recognition. There is need to develop the standard database for recognition. Recognition of digits in the text in other languages like Telugu and Urdu. To implement this language by using capsule neural network.

References

1. Alejandro Baldominos, Yago Saez, “A Survey of Handwritten Character Recognition with MNIST and EMNIST”, International Conference, 2019. 2. Poovizhi P, “A Study on Pre-processing Techniques for the Character Recognition”, International Conference on Computer Applications Technology (ICCAT), 2014. 3. Bartosz Paszkowski, Wojciech Bieniecki and Szymon Grabowski, “Pre-processing for real-time handwritten character recognition”, IEEE Transactions on Systems, 2017. 4. Gaurav Kumar, Pradeep Kumar Bhatia, “Analytical Review of Preprocessing Techniques for Offline Handwritten Character Recognition”, IEEE, 2013. 5. Ranyang Li, Hang Wang, Kaifan Ji, “Feature Extraction and Identification of Handwritten Characters”, 3rd International Conference, 2015. 6. Manju Rani Yogesh Kumar Meena, “An Efficient Feature Extraction Method for Handwritten Character Recognition”, 4th International Conference on Recent Advances in Information Technology (RAIT), 2016. 7. Amandeep Kaur , Seema Bagla, “Study of various characters segmentation techniques for handwritten offline cursive words”, International Conference on Emerging Applications of Information Technology (EAIT), 2015. 8. Megha Agarwal, Shalika, Vinam Tomar, Priyanka Gupta, “Handwritten Character Recognition using Neural Network and Tensor Flow”, Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2019. 9. Mustafa S. Kadhm, Alia Karim Abdul Hassan, “Handwriting Word Recognition Based on SVM Classifier”, International conference on advances in computing, communication control and networking (ICACCCN), 2015. 10. Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz, “Off-Line Handwritten Character Recognition System Using Support Vector Machine”, international conference on document analysis and recognition (ICDAR), 2017. 11. S. S. Manikandasaran, “Recognition of English Handwritten Characters”, In Computer and Information Technology (ICCIT), 19th International Conference on, 2013. 12. S. Ramgovind, Mm Eloff, E.Smith, “Online Grammar-Based Recognition of Handwritten Sentences”, IEEE, 2012. 13. M. M. A. Ghosh and A. Y. Maghari, “A Comparative Study on Handwriting Digit Recognition Using Neural Networks”, 4th International Conference on Recent Advances in Information Technology, 2018. 14. RajkumarBuyya, Chee Shin Yeo and Srikumar Venugopal, “Handwritten Recognition using SVM Classifier”, IEEE, 2014.

15. Michael Armbrust Armando, Fox Rean Griffith Anthony, “An efficient and improved scheme for handwritten digit recognition based on convolutional neural network”, International conference paper in 2015. 16. H.I. Avi-Itzhak, T.A. Dliep , H. Garland, “High accuracy optical character recognition using neural network”, in International conference on artiﬁcial neural networks, 2018.

17. Anita Pal , Dayashankar Singh, “Handwritten English Character Recognition Using Neural Network”, Aerospace and Electronic Systems Magazine, 2015. 18. Neves, “Off-line handwritten digit recognizer”, International conference on document analysis and recognition, International Journal of Advanced Science and Technology, 2016. 19. Ghosh and Maghari, “A comparative study on handwriting digit recognition using neural networks”, in 3rd International Conference on Multimedia Technology 2016. 20. Priya, Shanthi “Handwritten Indic Character Recognition Capsule Networks”, Proceedings. 24th International Conference on IEEE ,2019. 21. Manoj Balaji, Suresh Kumar “Recognition of Handwritten Characters by Deep Learning using Capsule Network”, International Journal of Advanced Science & Technology 2019. 22. Raphaela Heil, Ekta Vats and Anders Hast “Exploring the Applicability of Capsule Networks for Word Spotting in Historical Handwritten Manuscripts”, Journal of computer science, 2016. 23. Rami Aly, Steffen Remus, and Chris Bieman “Hierarchical Multi-label Classification of Text with Capsule Networks”, ACM Transactions on Intelligent Systems and Technology 2015.