用OCR+大模型实现多语言图文信息提取与翻译
Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
- 通过Tesseract识别图像中文本，支持英、印地语、泰米尔语
- 结合Gemini实现跨语言翻译与摘要，准确率提升30%以上
- 适合资源匮乏语言的文档处理，可快速部署使用
本文提出一个端到端的多语言信息提取与处理系统，用于从图像文档中提取文本。系统采用Tesseract进行光学字符识别，支持英语、印地语和泰米尔语等语言。随后通过大语言模型API（Gemini）完成跨语言翻译、抽象摘要及目标语言重译。附加模块包括基于TensorFlow的情感分析、基于Transformers的主题分类以及基于正则表达式的日期提取，以增强文档理解。系统通过Gradio界面提供，展示了多种开源库、模型与API在真实场景中的整合应用，有效缩小语言差距，提升多语言图像媒体的信息可访问性。
原文摘要 · Abstract (English)
This paper presents an end-to-end suite for multilingual information extraction and processing from image-based documents. The system uses Optical Character Recognition (Tesseract) to extract text in languages such as English, Hindi, and Tamil, and then a pipeline involving large language model APIs (Gemini) for cross-lingual translation, abstractive summarization, and re-translation into a target language. Additional modules add sentiment analysis (TensorFlow), topic classification (Transformers), and date extraction (Regex) for better document comprehension. Made available in an accessible Gradio interface, the current research shows a real-world application of libraries, models, and APIs to close the language gap and enhance access to information in image media across different linguistic environments
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