INTELLIGENT HANDWRITTEN TEXT RECOGNITION USING HYBRID CNN ARCHITECTURE BASED SVM CLASSIFIER WITH DROPOUT

Registration ID: IJNRD_196109

Published ID: IJNRD2305571

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C CHIDANAND, SAI BHARGAV SINGH K A, MUTHURAJ H R, .PAVAN KUMAR R B

Deep Learning,CNN,SVM,M3CE,ZCA

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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C CHIDANAND, SAI BHARGAV SINGH K A, MUTHURAJ H R, & .PAVAN KUMAR R B (May-2023). INTELLIGENT HANDWRITTEN TEXT RECOGNITION USING HYBRID CNN ARCHITECTURE BASED SVM CLASSIFIER WITH DROPOUT . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), f466-f469. https://ijnrd.org/papers/IJNRD2305571.pdf

Volume 8 Issue 5, May-2023

Pages : f466-f469

Paper Reg. ID: IJNRD_196109

Published Paper Id: IJNRD2305571

Research Area: Engineering

Author Type: Foreign Author

Country: -, -, -

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305571.pdf

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