Convolutional neural network (CNN)-based deep learning architectures are the state-of-the-art in image-based pattern recognition applications. The receptive filter fields in convolutional layers are learned from training data patterns automatically during classifier learning. There are number of well-defined, well-studied and proven filters in the literature that can extract informative content from the input patterns. This paper focuses on utilizing scattering transform-based wavelet filters as the first-layer convolutional filters in CNN architecture. The scattering networks are generated by a series of scattering transform operations. The scattering coefficients generated in first few layers are effective in capturing the dominant energy contained in the input data patterns. The present work aims at replacing the first-layer convolutional feature maps in CNN architecture with scattering feature maps. This architecture is equivalent to utilizing scattering wavelet filters as the first-layer receptive fields in CNN architecture. The proposed hybrid CNN architecture experiments the Malayalam handwritten character recognition which is one of the challenging multi-class classification problems. The initial studies confirm that the proposed hybrid CNN architecture based on scattering feature maps could perform better than the equivalent self-learning architecture of CNN on handwritten character recognition problems.

中文翻译：

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结合散射特征图和卷积神经网络进行马拉雅拉姆语手写字符识别

基于卷积神经网络（CNN）的深度学习架构是基于图像的模式识别应用程序中的最新技术。在分类器学习期间，自动从训练数据模式中学习卷积层中的接收滤波器字段。文献中有许多定义明确，经过充分研究和证明的过滤器可以从输入模式中提取有用的内容。本文着重于将基于散射变换的小波滤波器用作CNN体系结构中的第一层卷积滤波器。散射网络是通过一系列散射变换操作生成的。在前几层中生成的散射系数可有效捕获输入数据模式中包含的主要能量。本工作旨在用散射特征图代替CNN体系结构中的第一层卷积特征图。此架构等效于将散射小波滤波器用作CNN架构中的第一层接收场。拟议的混合CNN架构对Malayalam手写字符识别进行了实验，这是具有挑战性的多类分类问题之一。初步研究证实，所提出的基于散射特征图的混合CNN架构在手写字符识别问题上的性能要优于CNN的等效自学习架构。拟议的混合CNN架构对Malayalam手写字符识别进行了实验，这是具有挑战性的多类分类问题之一。初步研究证实，所提出的基于散射特征图的混合CNN架构在手写字符识别问题上的性能要优于CNN的等效自学习架构。拟议的混合CNN架构对Malayalam手写字符识别进行了实验，这是具有挑战性的多类分类问题之一。初步研究证实，所提出的基于散射特征图的混合CNN架构在手写字符识别问题上的性能要优于CNN的等效自学习架构。