MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Abstract—: “MODI lipi” is one of the Indian ancient scripts and is as yet unrecognized script. It is not in use today, but it has importance for historical researchers of not only the ancient Maratha history but history in different regions of India. The recognition of MODI demands transform invariant approach, as MODI documents are deformed severely. Invariant handwritten character recognition is accomplished in the past by employing feature extraction methods, yet there is a scope to improve the results under global transformations. At present, convolutional neural network exhibits only local transform invariance impulsively by convolution-pooling architecture and data augmentation. To achieve global invariance for MODI recognition, the proposed classification framework used CNN-based transfer learning and a global feature extractor histogram of oriented gradient. Additionally, the criterion based on principal component analysis and confusion matrix are introduced to choose the invariant feature and to find classes responsible for poor recognition rate. The proposed classifiers are trained on a self-created handwritten MODI character dataset and tested on transformed MODI dataset. The results showed that the proposed framework is effective to recognize MODI handwritten characters under transformations without data augmentation and network alteration. © 2022, Pleiades Publishing, Ltd.

School of Electronics & Communication Engineering

Engineering and Technology

World University root node

Pattern Recognition and Image Analysis

Convolutional Neural Network and Histogram of Oriented Gradient Based Invariant Handwritten MODI Character Recognition

&lt;!-- /* Font Definitions */ @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536869121 1107305727 33554432 0 415 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; text-autospace:none; font-size:10.0pt; font-family:"Cambria Math","serif"; mso-fareast-font-family:"Cambria Math"; mso-bidi-font-family:"Cambria Math"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Calibri; mso-fareast-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} .MsoPapDefault {mso-style-type:export-only; margin-bottom:10.0pt; line-height:115%;} @page WordSection1 {size:612.0pt 792.0pt; margin:72.0pt 72.0pt 72.0pt 72.0pt; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.WordSection1 {page:WordSection1;} --&gt; “MODI lipi” is one of the ancient scripts of Western India. Considerable work has been reported for various other ancient Indian languages except MODI lipi. Its structural characteristics and non-availability of image database make MODI recognition challenging. The work reported in this paper comprises creation of an image dataset for MODI handwritten characters and the development of a supervised Transfer Learning (TL) based classification framework. It makes use of Deep Convolutional Neural Networks (DCNN) Alexnet as a pre-trained network to transfer weights to retrain the network. This network is used as a feature extractor to extract features from different layers of the network. A Support Vector Machine (SVM) is trained on activation features to obtain classifier models. These models are investigated further for recognition accuracy and feature analysis. Subjective and objective measures are used to select discriminant deep features. We achieved recognition accuracies of 92.32% and 97.25% for Handwritten MODI character recognition and handwritten Devnagari character recognition respectively.

IETE Journal of Research

Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis

The contribution of this paper is to classify the images with transformation present in it. Transformations in the images are translation, rotation, scale and noise. The accuracy of classification decreases because of translations in the images. The proposed framework uses Convolutional Neural Network (CNN) for classification of transformed images. It consists of two convolutional neural networks, first CNN1, it is the standard CNN trained by images formed by data augmentation approach. Then its performance is tested for dataset of transformed images. The second one CNN2 is trained on only translated images. In the first stage, the images are classified using CNN1. And in the second stage, images misclassified by CNN1 are fed to CNN2 for further classification. The proposed framework is evaluated on hand written digits dataset-MNIST and alphabets dataset by NIST. The result shows that framework proposed outperforms single CNN. © 2018 IEEE.

2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 - Proceedings

A CNN based framework for translation invariant image classification

Handwritten character recognition of Devanagari script is an area of research in the field of pattern recognition. Feature extraction is crucially significant step in recognition system. In handwritten optical character recognition, the size of feature vectors is very high. By reducing image size, the dimension of feature vectors can be reduced but this also reduces the pixel information. This paper focus on the improvement in performance of recognition system using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In proposed system, first raw features are extracted using three different feature extraction methods: chain coding, edge detection using gradient features and direction feature techniques, which are reduced by LDA and characters are classified using SVM classifier. © 2018 IEEE.

Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018

Recognition of handwritten devanagari characters using linear discriminant analysis

Silicon photonics is achieving developments in <a href="https://www.sciencedirect.com/topics/engineering/optical-computing">optical computing</a> and information processing. It necessitates the efficient design of <a href="https://www.sciencedirect.com/topics/engineering/arithmetic-logic-unit">Arithmetic Logic Unit</a> (ALU) to perform optical micro-operations such as adder/subtractor, <a href="https://www.sciencedirect.com/topics/engineering/comparator">comparator</a>. The earlier work reported for ALU design used Mach-Zehnder Interferometer (MZI), <a href="https://www.sciencedirect.com/topics/engineering/semiconductor-optical-amplifier">semiconductor optical amplifiers</a>, <a href="https://www.sciencedirect.com/topics/engineering/terahertz">terahertz</a> optical asymmetric <a href="https://www.sciencedirect.com/topics/engineering/demultiplexer">demultiplexer</a> (TOAD) and <a href="https://www.sciencedirect.com/topics/engineering/beam-splitter">beam splitter</a>. These approaches make optical circuit larger in size and difficult to integrate with Very Large Scale <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/integrated-optics">Integrated Optics</a> (VLSIO). Here, we propose an all-optical ALU exclusively using Micro-Ring Resonator (MRR) to perform addition and comparison. The use of MRR to design ALU makes the proposed design novel and easy to integrate with VLSIO. The operations performed are one-bit addition and one-bit comparison. Beam splitter is replaced by MRR which has resulted in low intensity losses. The performance of the proposed ALU is discussed with simulation results and calculated losses. The output is verified for different input combinations of the half <a href="https://www.sciencedirect.com/topics/engineering/adders">adder</a> and comparator with electric field intensity distribution using 1550&nbsp;nm wavelength. More losses are observed in <a href="https://www.sciencedirect.com/topics/engineering/comparator-circuits">comparator circuit</a> as compared to <a href="https://www.sciencedirect.com/topics/engineering/adder-circuit">adder circuit</a>. Low losses and less MRR makes ALU <a href="https://www.sciencedirect.com/topics/engineering/optimised-design">design optimized</a>.