使用深度学习的盲文识别

Changjian Li, Weiqi Yan
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引用次数: 1

摘要

文本是传递和传递信息的媒介。盲文对于视障人士通过阅读和写作来交流信息是极其重要的。盲文翻译器是帮助人们理解盲文信息的关键工具。在本文中,我们使用ResNet实现基于字符的盲文翻译器,我们为盲文分类器实现了三个版本的ResNet,包括ResNet-18, ResNet-34和ResNet-50。我们还使用一种称为自适应贝塞尔曲线网络(ABCNet)的新颖解决方案实现了基于单词的盲文检测器,这是一种用于检测自然场景中基于单词的文本的场景文本识别(STR)方法。通过比较,对ABCNet的性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Braille Recognition Using Deep Learning
Text is the media to convey and transmit information. Braille is extremely important for vision impaired people to exchange information through reading and writing. A braille translator is crucial tool for aiding people to understand braille messages. In this paper, we implement character-based braille translator using ResNet, there are three versions of ResNet we implement for braille classifiers, including ResNet-18, ResNet-34, and ResNet-50. We also implement a word-based braille detector using a novel solution called Adaptive Bezier-Curve Network (ABCNet), which is a Scene Text Recognition (STR) method for detecting word-based text in natural scenes. A comparison is present to evaluate the performance of ABCNet.
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