集成CNN和变压器架构,以优越的阿拉伯印刷和手写字符分类
Mohammed R Al-Maamari1, Rakesh Ramteke1, Aymen M Al-Hejri2
1School of Computer Sciences, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, India.
Scientific reports
|August 15, 2025
概括
本研究介绍了一种混合变压器编码器模型,用于改进阿拉伯语光学字符识别 (OCR) 和手写字符识别,在复杂的阿拉伯文字挑战中实现高精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 阿拉伯文字给光学字符识别 (OCR) 带来了独特的挑战,因为它具有斜体的性质,双重字符和连字符.
- 精确的阿拉伯文本数字化转换对于教育,存档和信息检索中的应用至关重要.
研究的目的:
- 开发一个强大的混合模型,用于增强阿拉伯印刷和手写字符的分类.
- 为了解决现有方法在识别复杂的阿拉伯文字特征方面的局限性.
主要方法:
- 提出了一种基于混合变压器编码器的模型,将预先训练的VGG16和ResNet50模型的转移学习集成到特征提取中.
- 一个特征组合过程和变压器编码器的自我注意力机制被用来捕捉全球依赖.
- 该模型在阿拉伯语OCR和阿拉伯语手写字符识别 (AHCR) 数据集上进行了训练和评估.
主要成果:
- 该模型在阿拉伯语OCR数据集上达到99.51%,在AHCR数据集上达到98.19%的特殊准确率.
- 一项废除研究表明,与基于单个卷积神经网络 (CNN) 的模型和其他组合技术相比,其性能优越.
- 该模型在分类阿拉伯字符方面表现出强度和效率,包括对未见数据的性能.
结论:
- 拟议的混合变压器编码器模型在阿拉伯字符识别方面取得了重大进展.
- 这项研究为开发下一代阿拉伯语OCR系统提供了可靠的基础.
- 该模型的高精度和效率解决了阿拉伯文本识别中的现实世界挑战.
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