论文标题
跳过连接的多列网络，用于孤立的手写孟加拉语和数字识别
A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition
论文作者
论文摘要
在手写四个字符和/或数字中找到光学特征识别的局部不变模式是一项艰巨的任务。从一个人到另一个人的写作风格的变化使这项任务具有挑战性。我们已经在这项工作中使用多规模多列跳过卷积神经网络提出了一种非阐释特征提取方法。从所提出的体系结构的不同层中提取的本地和全局特征被合并，以得出编码角色或数字图像的最终功能描述符。我们的方法在四个孤立的手写孟加拉字符和数字的公开数据集上进行评估。针对当代方法的详尽比较分析确立了我们提出的方法的功效。
Finding local invariant patterns in handwrit-ten characters and/or digits for optical character recognition is a difficult task. Variations in writing styles from one person to another make this task challenging. We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network in this work. Local and global features extracted from different layers of the proposed architecture are combined to derive the final feature descriptor encoding a character or digit image. Our method is evaluated on four publicly available datasets of isolated handwritten Bangla characters and digits. Exhaustive comparative analysis against contemporary methods establishes the efficacy of our proposed approach.
