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An improved algorithm for recognizing mathematical equations by using machine learning approach and hybrid feature extraction technique

机译:一种改进的机器学习和混合特征提取技术识别数学方程的算法

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摘要

Handwritten mathematical symbols and equations recognition has captured a lot of concentration in the field of pattern recognition. Using efficient multilayer perceptron feed forward back propagation neural network with training algorithm gradient descent with momentum and adaptive learning definitely improve the performance and accuracy of proposed system. By considering hybrid feature in recognition system, the speed is enhanced with tremendous recognition accuracy. An experiment has been carried out for numerous kinds of equations in handwritten form and methodology has exposed successful results. In future, the proposed system might provide key factor to initiate for paperless environment by digitizing and transforming current paper documents.
机译:手写数学符号和方程式识别在模式识别领域已经引起了很多关注。使用有效的多层感知器前馈反馈神经网络和带动量的梯度下降训练算法以及自适应学习技术,可以显着提高所提出系统的性能和准确性。通过考虑识别系统中的混合特征,提高了速度,并具有巨大的识别精度。已经以手写形式对多种方程式进行了实验,并且方法论已经揭示了成功的结果。将来,拟议的系统可能会通过对当前纸质文档进行数字化和转换,为启动无纸化环境提供关键因素。

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