基于端到端可训练框架的无人机图像文本检测与识别系统

Qingtian Wu, Yimin Zhou, Guoyuan Liang
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引用次数: 4

摘要

在本文中,我们提出了一个基于dav的文本(主要是英语和汉语)检测和识别系统。将无人机与场景文本识别相结合,实现远程飞机图像的文本检测与识别,为无人导航和快速文本信息理解提供基础。健壮的文本检测和准确的文本识别可以通过两个贡献来实现。首先,提出了一种可扩展的引擎,通过将英文或中文文本以自然的方式叠加到现有图像中来合成文本图像。其次,采用卷积神经网络和递归神经网络相结合的端到端可训练框架,对变长文本进行高精度识别;在不同背景和室外拍摄的不同视频中进行了现场实验,结果表明该系统可以鲁棒有效地检测和识别无人机图像中的文本信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Text Detection and Recognition System Based on an End-to-End Trainable Framework from UAV Imagery
In this paper, we present a DAV-based system for text (mainly English and Chinese) detection and recognition. With the combination of unmanned aerial vehicle and scene text recognition, the system can realize text detection and recognition in long-range air-plane images, providing an underlay for unmanned navigation and fast text information understanding. Robust text detection and accurate text recognition can be achieved by two contributions. First, a scalable engine is proposed to synthesize text images by overlaying English or Chinese text into existing images in a natural way. Second, an framework which is trainable and end-to-end by combining Convolutional Neural Network and Recurrent Neural Network is adapted to recognize the variable-length text with a high accuracy. Field experiments are performed with different videos shot in various backgrounds and outdoors to show that the proposed system can detect and recognise text information in UAV imagery robustly and effectively.
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