面向移动设备的名片文本区域提取
Text Region Extraction from Business Card Images for Mobile Devices
摘要：
设计一个移动设备的名片阅读器（BCR）是一个挑战的研究人员，因为巨大的变形，在获得的图像，多样性的性质的名片，最重要的是移动设备的计算约束。本文提出了一种文本提取方法，设计在我们的工作对移动设备开发的BCR。首先，在粗略的水平上消除相机捕获图像的背景。然后，各种基于规则的技术应用于连接组件（CC），以过滤掉噪声和图像区域。识别为文本的CC然后使用自适应但轻量的二进制化技术进行二进制化。实验表明，文本提取的准确率在98%左右的分辨率范围内，具有不同的计算时间和内存需求。对于分辨率为1024x768像素的图像，文本提取精度为98.54%，空间和时间要求分别为1.1 MB和0.16秒，实现了最佳性能。
英文摘要：
Designing a Business Card Reader (BCR) for mobile devices is a challenge to the researchers because of huge deformation in acquired images, multiplicity in nature of the business cards and most importantly the computational constraints of the mobile devices. This paper presents a text extraction method designed in our work towards developing a BCR for mobile devices. At first, the background of a camera captured image is eliminated at a coarse level. Then, various rule based techniques are applied on the Connected Components (CC) to filter out the noises and picture regions. The CCs identified as text are then binarized using an adaptive but light-weight binarization technique. Experiments show that the text extraction accuracy is around 98% for a wide range of resolutions with varying computation time and memory requirements. The optimum performance is achieved for the images of resolution 1024x768 pixels with text extraction accuracy of 98.54% and, space and time requirements as 1.1 MB and 0.16 seconds respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
