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Jisuanji gongcheng yu sheji

Handwritten digital recognition method based on quantum neural networks

Li Peng

Published Jan 1, 2007• 2 citations
OpenAlex

Abstract

The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.

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The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
In this paper, a handwritten digital recognition system based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers is proposed. The recognition system proposed consists of two layer sub-classifiers, namely first-layer QNN coarse classifier and second-layer QNN numeral pairs classifier. Handwritten digital recognition experiments are performed by using data from MNIST database. Experiment results indicate the proposed QNN recognition system achieves excellent performance in terms of recognition rates and recognition reliability, and at the same time show the superiority and potential of QNN in solving pattern recognition problems.
The facial expression recognition is an important and complicated problem of pattern recognition field.An approach to facial expression recognition based on multi-level transfer function quantum neural networks(QNN)and multi-layer classifiers is presented in order to improve the recognition rate and recognition reliability.The QNN is trained and tested by the CMU face expression image database.The experiment results show that the method achieves excellent performance in terms of recognition rates and recognition reliability,and the superiority and potential of QNN in solving pattern recognition problems are abvious.
The Facial Expression recognition is an important and complicated problem of pattern recognition field.In this paper, an approach to Facial Expression recognition based on multi-level transfer function Quantum Neural Networks (QNN) and multi-layer classifiers is presented.The QNN is trained and tested by the Japanese female facial expression (JAFFE) database.The experiment results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks (QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Back-propagation (BP) algorithm and particle swarm optimization (PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.
In this paper, an approach to face recognition is presented, which based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. Firstly, image preprocessing is used to eliminate unrelated information in face images, which could help to locate eyes position. Secondly, feature extraction employs eigenface method based on Karhunen-Loeve transform to extract statistic feature and reduce dimensions. Finally, in the part of face recognition, quantum neural networks based on multi-level transform function are used. The QNN is trained and tested by the ORL faces digital database. At the same time with the classical BP neural network classifiers are compared, the results show that the identification method in more complex environments with a certain degree of robustness and effective and feasible.
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.