用小波变换检测复杂背景中的文字，不依赖字体大小，通用性强。
A Novel Framework For Text Detection From Natural Scene Images With Complex Background
- 通过小波变换和子带滤波提取图像特征，再聚类区域中心点定位文字。
- 在50张不同背景的图像上测试，对边缘明显的文字检测效果好。
- 方法可灵活适配不同应用场景，无需固定字体尺寸。
从相机拍摄的自然场景图像中识别文字是公认难题，主要源于背景复杂多变带来的检测困难。本文提出一种新颖高效的文本检测方法，利用小波变换处理灰度图像，结合子带滤波提取特征，再通过区域聚类技术基于区域质心定位文本区域，并为每个区域拟合边界框以识别文本位置。该方法不依赖特定字体大小，具有良好的泛化能力。实验采用50张具有多样化背景的图像作为样本集，涵盖边缘显著的图像。结果表明，该方法在复杂背景下表现优异，且可根据具体应用需求轻松定制。
原文摘要 · Abstract (English)
Recognizing texts from camera images is a known hard problem because of the difficulties in text detection from the varied and complicated background. In this paper we propose a novel and efficient method to detect text region from images with complex background using Wavelet Transforms. The framework uses Wavelet Transformation of the original image in its grayscale form followed by Sub-band filtering. Then Region clustering technique is applied using centroids of the regions, further Bounding box is fitted to each region thus identifying the text regions. This method is much sophisticated and efficient than the previous methods as it doesn't stick to a particular font size of the text thus, making it generalized. The sample set used for experimental purpose consists of 50 images with varying backgrounds. Images with edge prominence are considered. Furthermore, our method can be easily customized for applications with different scopes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。