• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A self controlled RDP approach for feature extraction in online handwriting recognition using deep learning
【24h】

A self controlled RDP approach for feature extraction in online handwriting recognition using deep learning

机译：利用深度学习的在线手写识别特征提取自控RDP方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The identification of accurate features is the initial task for benchmarked handwriting recognition. For handwriting recognition, the objective of feature computation is to find those characteristics of a handwritten stroke that depict the class of a stroke and make it separable from the rest of the stroke classes. The present study proposes a feature extraction technique for online handwritten strokes based on a self controlled Ramer-Douglas-Peucker (RDP) algorithm. This novel approach prepares a smaller length feature vector for different shaped online handwritten strokes without preprocessing and without any control parameter to RDP. Thus, it also overcomes the shortcomings of the traditional chain code based feature extraction approach that requires preprocessing of data, and the original RDP algorithm that requires a control parameter as an input to RDP. We further propose a deep learning network of 1-dimensional convolutional neural networks (Conv1Ds) for recognition, which trains in few minutes due to the smaller dimension of the convolution combined with smaller length feature vectors. The proposed approach can be applied to different scripts and different writing styles. The key aim of the present study is to provide a script independent feature extraction technique that is well suited for smaller devices. It improves the recognition over the best reported accuracy in the literature which was achieved using hidden Markov models with directional features, from 87.67% to 95.61% on a Gurmukhi dataset. For Unipen online handwriting datasets the results are at par with the literature.
机译：准确功能的识别是基准手写识别的初始任务。为了识别，特征计算的目的是找到描绘笔划的类的手写笔划的那些特征，并使其可与行程类的其余部分分开。本研究提出了一种基于自控式Ramer-Douglas-Peucker（RDP）算法的在线手写笔划特征提取技术。这种新颖的方法为不同形状的在线手写笔划准备了一个较小的长度特征向量，而无需预处理并且没有任何控制参数到RDP。因此，它还克服了需要预处理数据的传统链码的特征提取方法的缺点，以及要求控制参数作为RDP的输入的原始RDP算法。我们进一步提出了一种用于识别的1维卷积神经网络（CONV1DS）的深度学习网络，其由于卷积较小的尺寸与较小的长度特征向量组合而达到几分钟。所提出的方法可以应用于不同的脚本和不同的写入样式。本研究的关键目标是提供一种独立的特征提取技术，非常适合较小的设备。它提高了对使用定向特征的隐藏马尔可夫模型实现的文献中最好的报告准确性的识别，从87.67％到Gurmukhi数据集中的87.67％到95.61％。对于UniPen在线手写数据集，结果与文献相提并论。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号