深度学习CNN模型对Devanagari数字识别的评价

Kavita Bhosle, Vijaya Musande
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引用次数: 1

摘要

梵文的字符和数字识别是一项艰巨的任务,因为写作风格取决于一个人的特点,因人而异。由于深度学习卷积神经网络(cnn)的功能与人类大脑相似,我们在数字识别中得到了更精确的结果。在本研究中,将CNN方法付诸实践,并与前馈神经网络和随机森林方法进行对比。据报道,与之前的方法相比,CNN提供了高达99.2%的准确率。CNN对有组织和非结构化的数据都有效,包括图片、视频和音频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit
Devanagari character and digit recognition are a difficult undertaking because writing style depends on a person’s traits and differs from person to person. We get more precise results in digit recognition, thanks to deep learning convolutional neural networks (CNNs), which function similarly to the human brain. In this study, the CNN method was put into practice and contrasted with the feed-forward neural network and random forest approaches. In comparison to previous methods, CNN has reportedly provided an accuracy rating of up to 99.2%. CNN is effective with both organized and unstructured data, including pictures, video, and audio.
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