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

机译:一种通过机器学习方法和混合特征提取技术识别数学方程的改进算法

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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:手写的数学符号和方程识别已经捕获了模式识别领域的大量浓度。使用高效的多层的Perceptron馈送前后传播神经网络与训练算法梯度下降,随动力和自适应学习肯定提高了所提出的系统的性能和准确性。通过考虑识别系统中的混合特性,速度以巨大的识别准确性提高。对手写形式的许多方程进行了一个实验,方法论已经暴露了成功的结果。未来,建议的系统可能会通过数字化和转换当前纸质文件来为无纸环境发起的关键因素。

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