The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer. KeywordsOptical character recognition (OCR), Devanagari script, Numeral recognition, Character recognition, Convolutional neural network.

中文翻译：

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使用CNN的脱机手写梵文字符的多类识别

每个作者的笔迹风格都由变化，偏斜和倾斜的性质组成，因此，识别这些手写文档是一项令人振奋的任务。本文介绍了文献中可用于Devanagari手写字符识别的各种方法的研究，并使用卷积神经网络（CNN）进行了实现。研究了不同参数的可用方法，并进行了表格比较，得出了CNN模型在字符识别任务中的优越性。所提出的CNN模型使用压差和随机梯度下降（SGD）优化器可产生令人满意的精度。关键字光学字符识别（OCR），梵文脚本，数字识别，字符识别，卷积神经网络。