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This research paper explores the classification of handwritten digits using a multilayer neural network. The purpose is to develop a human verification system that can accurately recognize and validate a three-digit number written by users. To achieve this, the paper leverages OpenCV for image preprocessing, machine learning for digit prediction, and JavaScript for verification. The neural network consists of three hidden layers, and the Modified National Institute of Standards and Technology (MNIST) dataset, containing 70,000 handwritten digit images, is used for training and testing. The research achieved a remarkable 97.23% success rate in digit classification.

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Human Verification System Using Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Srinivas Konduri,
  • Sai Koushik Mupparapu

摘要

This research paper explores the classification of handwritten digits using a multilayer neural network. The purpose is to develop a human verification system that can accurately recognize and validate a three-digit number written by users. To achieve this, the paper leverages OpenCV for image preprocessing, machine learning for digit prediction, and JavaScript for verification. The neural network consists of three hidden layers, and the Modified National Institute of Standards and Technology (MNIST) dataset, containing 70,000 handwritten digit images, is used for training and testing. The research achieved a remarkable 97.23% success rate in digit classification.