Pattern Recognition is the method of mapping the inputs to their respective target classes based on features of data. In this paper a stacked ensemble meta-learning approach for customized convolutional neural network is proposed for Marathi handwritten numeral recognition. Stacked ensemble merges the pre-trained base pipe lines to create a multi-head meta-learning classifier that outputs the final target labels. It overpowers the average ensemble because the weighted and maximum contribution of each pipeline is taken in this approach. The stacked ensemble meta-learning classifier proves to be efficient because the base pipelines, which are already acquainted with output desirable results, are concatenated, instead of averaging, to achieve maximum efficiency. Performance evaluation and analysis have been done on Marathi handwritten numeral dataset, and the experiment results are better than the existing proposed systems.

模式识别是根据数据的特征将输入映射到它们各自的目标类的方法。在本文中，提出了一种用于马拉地语手写数字识别的定制卷积神经网络的堆叠集成元学习方法。Stacked ensemble 合并预先训练的基本管道以创建一个多头元学习分类器，输出最终目标标签。它压倒了平均集合，因为在这种方法中采用了每个管道的加权和最大贡献。堆叠集成元学习分类器被证明是有效的，因为已经熟悉输出理想结果的基础管道被连接起来，而不是平均，以实现最大效率。已经对马拉地语手写数字数据集进行了性能评估和分析，