综合框架利用场景文本检测和识别技术,以增强所有印第安语中名称板的兴趣点提取
Abhishek Kumar Kashyap1, Mahima Upadhya2, Vikas Singh Panwar3
1Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India. techmech46@gmail.com.
Scientific reports
|March 11, 2026
概括
这项研究增强了对印度文字的文字识别和从移动地图系统 (MMS) 图像中提取感兴趣点 (POI). 新的管道在脚本识别和文字识别方面实现了高精度,超过了以前的基准.
科学领域:
- 计算机视觉 计算机视觉
- 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:
- 移动地图系统 (MMS) 现有的计算机视觉模块需要进行增强,以提高功能和准确性.
- 目前的文本检测和识别 (TDR) 系统通过序列神经模块处理MMS图像.
研究的目的:
- 改进文本识别,脚本识别,语言分类和从MMS图像中提取感兴趣点 (POI).
- 为Indic脚本开发一个统一的管道,集成文字校正,OCR,检测和分类.
主要方法:
- 序列神经模块用于识别感兴趣的区域,检测文本单词,并裁剪相关的图像部分.
- 十种印度文字的脚本识别,其次是脚本特定的字符识别.
- 一个并行模块用于检测POI字段 (姓名,地址等). 为了提取全面的地址.
- 实施新的文本识别算法,脚本识别算法,基于字典的词检测和CRNN模型错误纠正.
主要成果:
- 在脚本识别中实现了96.17%的准确性.
- 在文字识别方面达到92.5%的准确性.
- 在POI检测中获得了33%的平均精度,超过了IndicText (93.6%) 和基于变压器的OCR (88.5%).
结论:
- 开发的框架显著提高了MMS中的AI模块处理Indic脚本的能力.
- 集成管道为从复杂的图像数据中提取文本和POI提供了更高的准确性和效率.
- 这项工作通过将多重视觉和NLP任务结合成一个单一的,印度语语言的专门管道,代表了一种新的方法.
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