Authors :
Abhishek Jha; Dr. Hitesh Singh; Dr. Vivek Kumar; Dr. Kumud Saxena
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/bdd5va6m
DOI :
https://doi.org/10.5281/zenodo.8181197
Abstract :
Biometric identification has emerged as a
powerful tool for recognizing individuals in various
applications, ranging from security systems to text
analysis. This paper focuses on the application of
biometric identification for recognizing text using a
combination of one-hot encoding and convolutional
neural networks (CNNs) through artificial intelligence
(AI). The one-hot encoding technique is employed to
represent textual data, where each word or character is
converted into a binary vector of zeros and ones. This
representation preserves the unique characteristics of
the text and enables efficient processing by the CNN
model. The CNN architecture is utilized to learn
meaningful features from the encoded text, capturing
important patterns and structures. The integration of AI
techniques further enhances the accuracy and efficiency
of the biometric identification system. AI algorithms
allow for the automatic extraction of relevant features,
reducing the need for manual feature engineering. The
trained CNN model is capable of recognizing text
patterns with a high degree of accuracy, enabling the
identification of individuals based on their unique
textual attributes. Experimental results demonstrate the
effectiveness of the proposed approach. The combination
of one-hot encoding and CNN via AI achieves notable
improvements in text recognition performance,
surpassing traditional methods. The system proves
robust to variations in text content, font styles, and sizes,
highlighting its potential for real-world applications.
Keywords :
CNN, AI, ONE-HOT ENCODING.
Biometric identification has emerged as a
powerful tool for recognizing individuals in various
applications, ranging from security systems to text
analysis. This paper focuses on the application of
biometric identification for recognizing text using a
combination of one-hot encoding and convolutional
neural networks (CNNs) through artificial intelligence
(AI). The one-hot encoding technique is employed to
represent textual data, where each word or character is
converted into a binary vector of zeros and ones. This
representation preserves the unique characteristics of
the text and enables efficient processing by the CNN
model. The CNN architecture is utilized to learn
meaningful features from the encoded text, capturing
important patterns and structures. The integration of AI
techniques further enhances the accuracy and efficiency
of the biometric identification system. AI algorithms
allow for the automatic extraction of relevant features,
reducing the need for manual feature engineering. The
trained CNN model is capable of recognizing text
patterns with a high degree of accuracy, enabling the
identification of individuals based on their unique
textual attributes. Experimental results demonstrate the
effectiveness of the proposed approach. The combination
of one-hot encoding and CNN via AI achieves notable
improvements in text recognition performance,
surpassing traditional methods. The system proves
robust to variations in text content, font styles, and sizes,
highlighting its potential for real-world applications.
Keywords :
CNN, AI, ONE-HOT ENCODING.
