用DBNet++和BART实现政府文档自动分类，支持图像与实时摄像头输入。
Automated document processing system for government agencies using DBNET++ and BART models
- 通过DBNet++检测复杂场景下的文字，BART完成四类文档分类。
- 在Total-Text数据集上文本检测准确率达92.88%，处理时长10小时。
- 适合需处理多种来源、低质量文档的政府机构自动化办公。
本文提出一种自动文档分类系统，可从图像中检测文本内容，并将文档分为发票、报告、信函和表格四类。系统支持离线图像（如U盘、硬盘、microSD卡）及实时摄像头捕获，能应对光照不均、任意角度、弯曲或部分遮挡、低分辨率和远距离文本等实际挑战。整体流程包括图像采集与预处理、基于DBNet++的文本检测[1]，以及基于BART的文本分类[2]，全部集成于使用Python与PyQt5开发的用户界面中。在包含高分辨率图像并模拟复杂挑战的Total-Text数据集上，系统文本检测准确率达到92.88%，累计处理时长10小时。结果表明该方法在非受限成像条件下对多源混合文档分类具有有效性。
原文摘要 · Abstract (English)
An automatic document classification system is presented that detects textual content in images and classifies documents into four predefined categories (Invoice, Report, Letter, and Form). The system supports both offline images (e.g., files on flash drives, HDDs, microSD) and real-time capture via connected cameras, and is designed to mitigate practical challenges such as variable illumination, arbitrary orientation, curved or partially occluded text, low resolution, and distant text. The pipeline comprises four stages: image capture and preprocessing, text detection [1] using a DBNet++ (Differentiable Binarization Network Plus) detector, and text classification [2] using a BART (Bidirectional and Auto-Regressive Transformers) classifier, all integrated within a user interface implemented in Python with PyQt5. The achieved results by the system for text detection in images were good at about 92.88% through 10 hours on Total-Text dataset that involve high resolution images simulate a various and very difficult challenges. The results indicate the proposed approach is effective for practical, mixed-source document categorization in unconstrained imaging scenarios.
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