基于YOLOv3算法的Baybayin书写系统光学字符识别

Angel Mikaela P. Ligsay, John B. Rivera, J. Villaverde
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摘要

在菲律宾,Baybayin是一种起源于前西班牙殖民时期的书写系统。这个有着数百年历史的书写系统获得了关注和普及,后来在2018年被批准为法案。最近,将Baybayin文字翻译成全球通用的文字系统“字母”的研究进展,使用了人工智能(ai)。不同的研究人员开发了Baybayin文字的光学字符识别系统,但无法在一张图像中翻译多个字符,而且都使用了对象分类算法。因此,需要一种使用基于YOLOv3的CNN架构的系统,能够识别word形式的Baybayin脚本。使用YOLOv3算法,系统能够达到98.92%的准确率。据观察,有些分类错误是由于字迹扭曲或难以辨认所致。由此可见,使用YOLOv3算法对Baybayin字符进行光学字符识别,在Baybayin字符的检测和分类方面具有较高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical Character Recognition of Baybayin Writing System using YOLOv3 Algorithm
In the Philippines, Baybayin is one of its writing systems that originated in pre-Hispanic colonialism. The centuries-old writing system gained attention and popularity, which later turned into an approved bill in 2018. The recent development of research aimed at translating Baybayin characters into Alphabets, the globally recognizable writing system, uses Artificial Intelligence or A.I. Different researchers have developed an optical character recognition system for the Baybayin script but are incapable of translating multiple characters in single image and are all using object classification algorithms. Therefore, there is a need for a system using a YOLOv3 based CNN architecture capable of recognizing Baybayin scripts in word form. Using the YOLOv3 algorithm, the system was able to achieve an accuracy of 98.92%. It was observed that some of the misclassifications are due to distorted or illegible handwriting. It can be concluded that the optical character recognition of Baybayin characters using the YOLOv3 algorithm is of high accuracy when it comes to detecting and classifying Baybayin characters.
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