ABSTRACT Handwritten optical character recognition (OCR) is the renowned research area in several fields, like writers identification, bank cheques, and so on. Literature works presented the handwritten OCR for various languages. This paper proposes a hybrid neural network training algorithm for English handwritten OCR. Initially, the noise in the input image is removed using the median filter, and the image is resized. Then, the feature sets, positional, and structural descriptors are extracted from the input image. Once the feature sets are extracted, the proposed FLM-based neural network identifies the handwritten character. The FLM proposed by combining the Firefly and the Levenberg–Marquardt (LM) algorithm for training the neural network. Finally, the proposed FLM-based neural network is integrated within the feed forward neural network, and the classification of character is done with 95% accuracy based on the size of training data, number of hidden neurons and number of hidden layers.

中文翻译：

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基于混合神经网络训练算法的手写光学字符识别

摘要 手写光学字符识别 (OCR) 是多个领域的著名研究领域，如作者身份识别、银行支票等。文学作品呈现了各种语言的手写OCR。本文提出了一种针对英文手写OCR的混合神经网络训练算法。最初，使用中值滤波器去除输入图像中的噪声，并调整图像大小。然后，从输入图像中提取特征集、位置和结构描述符。一旦提取了特征集，提出的基于 FLM 的神经网络就会识别手写字符。FLM 通过结合 Firefly 和 Levenberg-Marquardt (LM) 算法来训练神经网络。最后，将提出的基于 FLM 的神经网络集成到前馈神经网络中，