【摘要】 透明文字信息提取在商品包装、广告设计和物流追踪等领域具有重要应用价值。然而，透明文字的低对比度、透明度变化及背景干扰，使传统OCR技术面临挑战。为此，研究提出一种结合多步骤图像预处理与深度学习OCR的优化方法。通过Gamma校正与CLAHE增强亮度和对比度，并结合HSV颜色空间分割与形态学处理，提高透明文字区域的显著性并抑制背景噪声。实验结果表明，该方法显著提升了识别精度和字符连贯性，为复杂背景下的透明文字提取提供了创新方案，并为信息自动化处理提供技术支持。更多还原

【Abstract】 Transparent text extraction has important applications in fields such as product packaging,advertising design,and logistics tracking. However,the low contrast of transparent text,variations in transparency,and background interference pose challenges for traditional OCR technology. To address this issue,this study proposes an optimized method that integrates multi-step image preprocessing with deep learning-based OCR. Gamma correction and CLAHE are used to enhance brightness and contrast,while HSV color space segmentation and morphological processing improve the saliency of transparent text regions and suppress background noise. Experimental results show that this method significantly improves recognition accuracy and character coherence,providing an innovative solution for transparent text extraction in complex backgrounds and offering technical support for automated information processing.更多还原