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Ms.J.Pavithra, S. Abhilash, S.Karthick, M.Karthikeyan
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Page No: 1 - 7
Abstract : Optical Character Recognition (OCR) is a key research area in the field of machine learning and artificial intelligence, with significant applications in digitizing handwritten documents, postal systems, banking, and more. The advent of Convolutional Neural Networks (CNNs) has significantly improved the accuracy and efficiency of handwritten character recognition. Traditional methods, which relied on feature extraction and classification techniques, have struggled to maintain high accuracy across different writing styles and diverse character sets. In contrast, CNNs excel at learning hierarchical features directly from raw pixel data, making them highly effective for image-based tasks such as character recognition. This paper explores the use of CNN algorithms for handwritten character recognition, providing an in-depth analysis of existing systems, their limitations, and the proposed improvements. By utilizing deep learning techniques, the model improves upon previous systems by achieving higher accuracy, robustness against noisy data, and greater adaptability to various writing styles. Additionally, the proposed system integrates data augmentation techniques to further enhance the model's generalization capability, enabling it to perform well on unseen handwritten samples.
Keyword: CNN architecture for character recognition, OCR using convolutional neural networks, deep learning OCR handwritten, CNN for OCR, document digitization with CNN, banking OCR automation CNN, OCR in postal systems deep learning.
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Optical Character Recognition (OCR) is the automated process identifying handwritten
characters, with uses in document digitization, form processing, and more. Handwriting
recognition is challenging due to variations in style, distortions, and noise. Early OCR relied on
manual feature extraction and classical machine learning algorithms like K-NN, SVM, and
decision trees, which performed well on controlled data but struggled with real-world
variability. The advent of Convolutional Neural Networks (CNNs) has significantly improved OCR
accuracy. CNNs automatically learn features from raw images, eliminating the need for manual
extraction. Their architecture comprising convolutional, pooling, and fully connected layers
enables them to detect patterns, reduce data complexity, and classify characters effectively.
CNNs handle diverse handwriting styles and noisy inputs better than traditional methods and
scale well across languages and writing systems OPTICAL CHARACTER RECOGNITION PREDICTION
1.CONVOLUTIONAL NEURAL NETWORK CNNs are powerful neural networks designed
specifically for processing grid-like data, such as images. These models are especially effective
in visual recognition tasks due to their ability to automatically detect essential patterns within
images. By applying filters that scan across the image in horizontal and vertical directions, CNNs
capture distinct features edges, textures, shapes which are later used to identify the contents
of the image, be it a human organ, an anomaly, or something else entirely. CNNs are resilient to
transformations such as rotation, scaling, or translation, making them well-suited for real-world
image data.

Machine Learning

Machine Learning (ML) is a branch of AI that enables computers to learn
from data and make predictions or decisions without explicit programming OPTICAL CHARACTER RECOGNITION PREDICTION. Unlike traditional
programming, where rules are predefined, ML algorithms learn patterns from data to improve
performance over time. For example, instead of teaching a computer what a cat looks like, ML
uses thousands of cat images to learn distinguishing features on its own OPTICAL CHARACTER RECOGNITION PREDICTION. This self-improving
ability powers technologies like voice assistants, recommendation systems, self-driving cars,
and predictive analytics.

OPTICAL CHARACTER RECOGNITION PREDICTION

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    models. The dataset comprises admission scores, first-level course results, AAT, and GAT
    scores. The research highlights the merit of early identification of performance issues
    and helps in mitigating failure risks. However, it falls short as it cannot combine non-
    academic features with academic ones OPTICAL CHARACTER RECOGNITION PREDICTION.

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Fig.1Module Design OPTICAL CHARACTER RECOGNITION PREDICTION