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首页> 外文会议>Conference on Document Recognition and Retrieval Ⅷ Jan 24-25, 2001, San Jose, USA >A gray-scale image based character recognition algorithm to low quality and low-resolution images
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A gray-scale image based character recognition algorithm to low quality and low-resolution images

机译：基于灰度图像的低质量低分辨率图像字符识别算法

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Character recognition in low quality and low-resolution images is still a challenging problem. In this paper a gray-scale image based character recognition algorithm is proposed, which is specially suit to gray scale images captured from real world or very low quality character recognition. In our research, we classify the deformations of the low quality and low-resolution character images into two categories: (a) High spatial frequency deformations derived from either the blur distortion by the point spread function (PSF) of scanners or cameras, random noises, or character deformations; (b) Low spatial frequency deformations mainly derived from the large-scale background variations. The traditional recognition methods based on binary images cannot give satisfactory results in these images because these deformations will result in great amount of strokes touch or stroke broken in the binarization process. In the proposed method, we directly extract transform features on the gray-scale character images, which will avoid the shortcomings produced by binarization process. Our method differs from the existing gray-scale methods in that it avoids the difficult and unstable step of finding character structures in the images. By applying adequate feature selection algorithms, such as linear discriminant analysis (LDA) or principal component analysis (PCA), we can select the low frequency components that preserve the fundamental shape of characters and discard the high frequency deformation components. We also develop a gray-level histogram based algorithm using native integral ratio (NIR) technique to find a threshold to remove the backgrounds of character images while maintaining the details of the character strokes as much as possible. Experiments have shown that this method is especially effective for recognition of images of low quality and low-resolution.
机译：低质量和低分辨率图像中的字符识别仍然是一个具有挑战性的问题。本文提出了一种基于灰度图像的字符识别算法，该算法特别适用于从现实世界或非常低质量的字符识别中捕获的灰度图像。在我们的研究中，我们将低质量和低分辨率字符图像的变形分为两类：（a）由扫描仪或照相机的点扩散函数（PSF）引起的模糊失真，随机噪声引起的高空间频率变形或字符变形； （b）低空间频率变形主要来自大规模背景变化。传统的基于二值图像的识别方法无法在这些图像中获得令人满意的结果，因为这些变形会导致在二值化过程中出现大量笔触或笔触破裂。在该方法中，我们直接在灰度字符图像上提取变换特征，避免了二值化过程产生的缺点。我们的方法不同于现有的灰度方法，因为它避免了在图像中查找字符结构的困难和不稳定的步骤。通过应用适当的特征选择算法，例如线性判别分析（LDA）或主成分分析（PCA），我们可以选择保留字符基本形状的低频成分，并丢弃高频变形成分。我们还开发了一种基于灰度直方图的算法，该算法使用本机积分比（NIR）技术来查找阈值，以去除字符图像的背景，同时尽可能保留字符笔触的细节。实验表明，该方法对于识别低质量和低分辨率的图像特别有效。

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