Hand written text recognition

Registration ID: IJNRD_190583

Published ID: IJNRD2304132

DOI: Click Here to Get

N SAI SREE , U SAI KIRAN , UDAY KIRAN

Hand written text recognition, LSTM,CNN,RESNET

The goal of this paper is to categorize every distinct handwritten word, making it possible to digitize handwritten images. We primarily used character segmentation and direct categorization of words techniques to finish this study. To create a model that can accurately characterize words, we can create a convolutional neural network for the former(CNN) with a variety of alternative topologies. For the latter, we use convolutional and long Short-Term Memory networks(LSTM) to generate bounding boxes for each character. The characters are segmented before being sent to CNN for classification. Finally, using the data from classification and segmentation, we recreate each word.

Published Paper   E-Certificate

N SAI SREE, U SAI KIRAN, & UDAY KIRAN (April-2023). Hand written text recognition. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), b283-b286. https://ijnrd.org/papers/IJNRD2304132.pdf

Volume 8 Issue 4, April-2023

Pages : b283-b286

Paper Reg. ID: IJNRD_190583

Published Paper Id: IJNRD2304132

Downloads: 000121986

Research Area: Computer Science & Technology 

Country: Vishakapatnam, Andhra pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304132.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304132

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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