The problem of searching a similar pattern is an exciting and challenging research field of pattern recognition. The intelligence of humans for vision to read is a crucial phenomenon for machine simulation and has been carried out for a few decades. Therefore, in this article, a recognition system of handwritten Meitei Mayek (Manipuri script) is introduced using a convolutional neural network. Generally, character recognition is performed using the gray scale of the image of characters. However, we have additionally considered the corresponding gradient direction and gradient magnitude images to create three‐channels image for every character so that supplementary information from gradient images can be obtained for efficient recognition. Experiments are conducted on 14 700 sample images collected from various individuals of different age groups and educational backgrounds. A recognition rate of 98.70% is obtained, which is compared with the existing methods, and it is found to be superior performance than other neural network methods on Meitei Mayek.

中文翻译：

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使用梯度和灰色三通道卷积神经网络的手写Meitei Mayek识别

搜索相似模式的问题是模式识别的一个激动人心且具有挑战性的研究领域。人类对于视觉阅读的智能是机器仿真的关键现象，并且已经进行了几十年。因此，在本文中，使用卷积神经网络介绍了手写Meitei Mayek（Manipuri脚本）的识别系统。通常，使用字符图像的灰度来执行字符识别。但是，我们还考虑了相应的梯度方向和梯度幅值图像，以便为每个字符创建三通道图像，以便可以从梯度图像中获得补充信息，以进行有效识别。对从不同年龄段和教育背景的各个个体收集的14 700个样本图像进行了实验。与现有方法相比，获得了98.70％的识别率，并且发现其在Meitei Mayek上的性能优于其他神经网络方法。