...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ACM transactions on Asian language information processing >Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network
【24h】

Handwritten Manipuri Meetei-Mayek Classification Using Convolutional Neural Network

机译：卷积神经网络的手写Manipuri Meetei-Mayek分类

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

A new technique for classifying all 56 different characters of the Manipuri Meetei-Mayek (MMM) is proposed herein. The characters are grouped under five categories, which are Eeyek Eepee (original alphabets), Lom Eeyek (additional letters), Cheising Eeyek (digits), Lonsum Eeyek (letters with short endings), and Cheitap Eeyek (vowel signs. Two related works proposed by previous researchers are studied for understanding the benefits claimed by the proposed deep learning approach in handwritten Manipuri Meetei-Mayek. (1) Histogram of Oriented (HOG) with SVM classifier is implemented for thoroughly understanding how HOG features can influence accuracy. (2) The handwritten samples are trained using simple Convolutional Neural Network (CNN) and compared with the proposed CNN-based architecture. Significant progress has been made in the field of Optical Character Recognition (OCR) for well-known Indian languages as well as globally popular languages. Our work is novel in the sense that there is no record of work available to date that is able to classify all 56 classes of the MMM. It will also serve as a pre-cursor for developing end-to-end OCR software for translating old manuscripts, newspaper archives, books, and so on.
机译：本文提出了一种对Manipuri Meetei-Mayek（MMM）的所有56个不同字符进行分类的新技术。这些字符分为五个类别，分别是Eeyek Eepee（原始字母），Lom Eeyek（附加字母），Cheising Eeyek（数字），Lonsum Eeyek（短尾字母）和Cheitap Eeyek（元音符号。已提出两项相关作品）以前的研究人员进行了研究，以了解手写的Manipuri Meetei-Mayek所提出的深度学习方法所带来的好处。（1）利用SVM分类器实现了定向直方图（HOG），以彻底了解HOG功能如何影响准确性（2）。手写样本使用简单的卷积神经网络（CNN）进行了训练，并与拟议的基于CNN的体系结构进行了比较，在著名的印度语言和全球流行语言的光学字符识别（OCR）领域取得了重大进展。 。从某种意义上说，我们的作品是新颖的，因为迄今为止尚无能够对MMM的所有56个类别进行分类的作品记录。用于开发端到端OCR软件的预兆，用于翻译旧手稿，报纸档案，书籍等。

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