使用更快的 R-CNN 检测爪哇语字母

Muhammad Helmy Faishal, M. D. Sulistiyo, Aditya Firman Ihsan
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引用次数: 0

摘要

爪哇文现在已经很少使用,有些人已经不认识它了。建立基于数字图像处理的爪哇语文字识别系统是其保护工作之一。本研究提出了一个能够使用 Faster R-CNN 检测和识别爪哇文字的模型,以帮助不熟悉爪哇文字的人们。之所以选择 Faster R-CNN,是因为与之前的方法相比,它不需要额外的处理,而且 Faster R-CNN 具有更好的准确性和检测小物体的能力。Faster R-CNN 在文本检测中显示出良好的效果,但 Faster R-CNN 在爪哇语文字检测中的应用尚未发现,因此其性能尚不清楚,本研究将展示 Faster R-CNN 在爪哇语文字检测中的表现。在本研究中,Faster R-CNN 的平均精确度 (mAP) 值高达 0.8381,准确率高达 96.31%,精确度高达 96.53%,召回率高达 96.38 %,F1 分数高达 96.41%,表现出了良好的性能。这些结果表明,Faster R-CNN 比之前的方法效果更好,能很好地检测爪哇语字符。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Javanese Script Letter Detection Using Faster R-CNN
The Javanese script is now rarely used, and some people no longer recognize it. The construction of a Javanese script recognition system based on digital image processing is one of its preservation efforts. This study proposes a model capable of detecting and recognizing Javanese characters using Faster R-CNN to help people who are not familiar with the Javanese script. Faster R-CNN was chosen because it does not require additional processing compared to the previous method and Faster R-CNN has better accuracy and the ability to detect small objects. Faster R-CNN shows good results in text detection, but the use of Faster R-CNN in detecting Javanese script has not been found which makes its performance unknown, so this study will show how Faster R-CNN performs in detecting Javanese script. In this study, Faster R-CNN was able to show good performance by obtaining mean average precision (mAP) values up to 0.8381, accuracy up to 96.31%, precision up to 96.53%, recall up to 96.38 %, and F1-Score up to 96.41%. These results indicate that Faster R-CNN has better results than the previous method and can detect Javanese characters well.
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