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深度初始神经网络与残余连接用于泰米尔手写字符识别
Hariharan Periyasamy1, Sasikaladevi Natarajan2, Rengarajan Amirtharajan3
1Department of Computer Science and Engineering, School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613401, India.
Scientific reports
|January 23, 2026
概括
本研究介绍了TamHNet,这是一个新的深度学习模型,用于泰米尔手写字符识别 (THCR). 通过使用微调的Inception-ResNet架构和Adam优化器来实现高效的字符分类,TamHNet实现了99.8%的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 模式识别 模式识别
背景情况:
- 通过光学字符识别 (OCR) 数字化文本对于保护文化遗产和有效检索信息至关重要.
- 由于字符之间的微妙差异,泰米尔手写字符识别 (THCR) 提出了独特的挑战.
研究的目的:
- 为泰米尔手写字符识别 (THCR) 开发一个高效和准确的系统.
- 为了应对剧本复杂的角色变化所带来的挑战.
主要方法:
- 为THCR提出了一个带有残余连接的深度开始神经网络框架 (TamHNet).
- 非线性双边过器用于图像预处理和Inception-ResNet架构的域自适应微调.
- 训练是在使用Adam优化器对自定义的泰米尔隔离字符数据集 (TICD) 进行的.
主要成果:
- 在THCR任务中,TamHNet模型实现了99.8%的高精度.
- 微调的架构展示了优越的特征歧视和识别性能.
- 亚当优化器在训练过程中促进了更快的融合和稳定性.
结论:
- TamHNet为泰米尔手写字符识别提供了强大的,高度准确的解决方案.
- 域自适应微调是有效的提高性能复杂的脚本,如泰米尔.
- 拟议的系统的性能优于THCR中现有的最先进的方法.
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