Abstract:Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human-robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

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The 2019 novel coronavirus (2019-nCoV), with a beginning stage in Wuhan (China), has spread quickly among people living in different nations and has affected more than 6 million lives. Researchers around the globe are trying to device a solution in the form of the vaccine but unfortunately, till date, there is no full-proof vaccine for 2019-nCoV. Most of the countries are adopting prolonged lockdown and social distancing strategies to counter this pandemic. Flying Ad-hoc Network (FANET) on the other hand, can provide several services such as the delivery of essential items, disinfecting common areas, surveillance, traffic monitoring, communication, temperature monitoring, etc. Availing such services using FANETs during Corona Virus Disease (COVID-19) outbreak is like a boon that can minimize the general problems of mankind to some extent during lockdowns. Nowadays, it is foremost required to deploy solutions that can automate the process of availing several services using FANETs and the same is attracting researchers in great demand. This paper has presented a brief survey of several applications of FANETs (especially UAVs and Drones) that can facilitate mankind in coping with the general problems during COVDI-19 outbreak and subsequent lockdowns. We hope that this survey has covered most of the applications of UAVs and can provide new insights into the research community.

The discussion in the paper is regarding to the recognition of handwritten Devanagari vowels by means of a classifier named as K-NN (K-Nearest Neighbour). Before applying classifier, feature extortion is accomplished for extracting the feature points (FP) i.e. also known as division points (DP). In this paper the feature extortion is perform through recursive sub division technique, which is first time implemented on Devanagari vowels. K-NN classifier is functioned for the learning and the testing phases, through which the recognition go ahead to the high performances in terms of recognition rate, pre-processing and classification speed. Authors tested the described approach using the ISI (Indian Statistical Institute), Kolkata"s handwritten Devanagari vowels database containing 9191 samples, which is divided into 1:3 as testing and training samples respectively. In the recognition process using K-NN classifier 88 vowels are total wrongly identified out of 2281vowels. The recognition rate comes out to be 96.14%.