Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece.
IEEE Trans Image Process. 2011 Apr;20(4):910-20. doi: 10.1109/TIP.2010.2080280. Epub 2010 Sep 27.
Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D rectangular area. The projection of the curved surface on the plane is guided only by the textual content's appearance in the document image while incorporating a transformation which does not depend on specific model primitives or camera setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the document image. Experimental results on various document images with a variety of distortions demonstrate the robustness and effectiveness of the proposed rectification methodology using a consistent evaluation methodology that encounters OCR accuracy and a newly introduced measure using a semi-automatic procedure.
文档的数字化无论是使用平板扫描仪还是基于摄像头的系统，都会导致文档图像产生扭曲和透视变形等问题，从而降低当前光学字符识别（OCR）方法的性能。在本文中，我们提出了一种面向目标的校正方法，以补偿文档图像的不良变形，从而提高 OCR 结果的质量。我们的方法依赖于一种从粗到精的策略。首先，借助一种计算成本低的变换来完成粗略校正，该变换解决了将曲面投影到 2D 矩形区域的问题。曲面在平面上的投影仅由文档图像中文本内容的外观引导，同时采用一种不依赖于特定模型基元或相机设置参数的变换。其次，在单词级别应用位姿归一化，以恢复文档图像的所有局部变形。在各种具有不同变形的文档图像上进行的实验结果表明，所提出的校正方法具有很强的鲁棒性和有效性，并且使用了一致的评估方法来衡量 OCR 精度和使用半自动过程引入的新度量。
