Paper Title
INTELLIGENT HANDWRITTEN TEXT RECOGNITION USING HYBRID CNN ARCHITECTURE BASED SVM CLASSIFIER WITH DROPOUT
Article Identifiers
Authors
C CHIDANAND , SAI BHARGAV SINGH K A , MUTHURAJ H R , .PAVAN KUMAR R B
Keywords
Deep Learning,CNN,SVM,M3CE,ZCA
Abstract
Handwritten text recognition is a challenging task in the field of pattern recognition and artificial intelligence. This research paper proposes a novel approach for intelligent handwritten text recognition by integrating a hybrid architecture of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) classifier with dropout regularization. These identification algorithms must overcome various obstacles, including as vast open data-bases, an endless range of handwriting styles, and freestyle The authors have also put forth a ground-breaking depth neural network training rule for maximum interval minimal classification error in light of the analysis of the error backpropagation method. On the databases AHDB, AHCD, HACDB, and IFN/ENIT, authors tested the suggested model.
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How To Cite
"INTELLIGENT HANDWRITTEN TEXT RECOGNITION USING HYBRID CNN ARCHITECTURE BASED SVM CLASSIFIER WITH DROPOUT ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 5, page no.f466-f469, May-2023, Available :https://ijnrd.org/papers/IJNRD2305571.pdf
Issue
Volume 8 Issue 5, May-2023
Pages : f466-f469
Other Publication Details
Paper Reg. ID: IJNRD_196109
Published Paper Id: IJNRD2305571
Downloads: 000121111
Research Area: Engineering
Country: -, -, -
Published Paper PDF: https://ijnrd.org/papers/IJNRD2305571.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305571
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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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