Recognition of handwritten characters in two Indic scripts Bangla and Meitei Mayek is one of the challenging responsibilities due to intricate patterns and scarcity of standard datasets. Convolutional Neural Network (CNN) is one of the stablest well-known techniques for classifying objects in distinctive specialties as it has an extraordinary capability of discovering complex patterns. In this paper, we hook a different layout and obtain a unique CNN architecture from scratch, which has manifold advantages over classical machine learning (ML) approaches, and it has a unique ability to consolidate feature extraction and classification altogether. Further, we stretch our work to uncover the mathematical rationale for using non-linearity in the deep learning (DL) model. Our proposed CNN architecture consists of four layers, including convolutional layer (CL), nonlinear activation layer (AL), pooling layer (PL), and fully connected layer (FCL), which are used in the existing two accessible Bangla datasets named cMATERdb and ISI Bangla datasets. The identical model also validates on proposed Manipuri Character dataset, called “Mayek27”. Moreover, we perform an in-depth comparison with different batch sizes and optimization techniques over all the datasets for understanding their functionality. We conceive a novel benchmark performance that has delivered state-of-the-art decisions on two regional handwritten character identifications.

由于复杂的模式和标准数据集的稀缺性，在两个印度文字中，Bangla和Meitei Mayek的手写字符识别是具有挑战性的职责之一。卷积神经网络（CNN）是最稳定的知名技术，用于对特殊专业中的对象进行分类，因为它具有发现复杂模式的非凡能力。在本文中，我们采用了不同的布局，并从零开始获得了独特的CNN架构，它具有比经典机器学习（ML）方法更多的优势，并且具有将特征提取和分类完全整合在一起的独特能力。此外，我们扩展了我们的工作以揭示在深度学习（DL）模型中使用非线性的数学原理。我们提议的CNN架构包括四层，cMATERdb和ISI Bangla数据集。相同的模型还可以在提议的Manipuri Character数据集上进行验证，该数据集称为“ M a y e k 27”。此外，我们对所有数据集使用不同的批次大小和优化技术进行了深入比较，以了解其功能。我们构想出一种新颖的基准性能，它在两个区域手写字符识别方面提供了最新的决策。