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Author: search.filters.author.Kadam, N.

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    Handwritten Text Recognition from an Image with Android Application
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mule, H.; Kadam, N.; Naik, D.
    Nowadays, Storing information from handwritten documents for future use is becoming necessary. An easy way to store information is to capture handwritten documents and save them in image format. Recognizing the text or characters present in the image is called Optical Character Recognition. Text extraction from the image in the recent research is challenging due to stroke variation, inconsistent writing style, Cursive handwriting, etc. We have proposed CNN and BiLSTM models for text recognition in this work. This model is evaluated on the IAM dataset and achieved 92% character recognition accuracy. This model is deployed to the Firebase as a custom model to increase usability. We have developed an android application that will allow the user to capture or browse the image and extract the text from the picture by calling the firebase model and saving text in the file. To store the text file user can browse for the appropriate location. The proposed model works on both printed and handwritten text. © 2022 IEEE.
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    Multiple Choice Question Answering Using Attention Based Ranking and Transfer Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kadam, N.; Anand Kumar, A.M.
    The multiple choice question answering is still considered as an challenging task in Natural Language Processing. In this paper, we have tried to solve the problem of answering multiple choice questions where supporting documents corresponding to each question are not explicitly provided. Context retrieval is the strategy, which focuses on both reasoning and retrieving better supporting contexts. We present a improvised version of attention based deep neural network that eventually learns to order documents according to their relevance in relation to a given topic, all while achieving the goal of predicting the correct response. The top documents retrieved are considered more relevant context for given question answer pair. To achieve more accurate results transformer based pre-trained models are used in the implementation. We have used the concept of transfer learning which is related to learning and adapting knowledge by fine tuning model on other datasets. The reasoning challenge dataset by Allen institute is used to test the approach and SQuAD 2.0 and RACE datasets are used to fine tune the transformer based models. The accuracy of proposed model on ARC easy dataset is 89.51% and on ARC challenge dataset is 62.53%. © 2022 IEEE.