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Performance Evaluation of Image Binarization Technique for Recognition of Ancient Historical Documents

机译:图像二值化技术在古代史料识别中的性能评估

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Information acquisition from degraded historical document has always been a challenging task due to various forms of degradation. Image binarization is very much essential in the restoration of degraded historical documents. Eventhough many algorithms have been proposed, still there is a need for an effective algorithm to solve all kind of degradation problems. In this paper ,an optimum binarization technique is proposed that addresses these issues by using a combined approach of local image contrast and gradient . Firstly,the input image is binarized and then canny egde map is applied to extract text stroke edge pixels.To enhance further,morphological operations are carried out based on shapes.Finally an adaptive thresholding is applied to segment foreground and background pixels.To determine the quality, the proposed method has been tested on 3 public data sets (DIBCO 2009, 2010, 2011) that were taken from pattern recognition and image analysis(PRImA) research lab. Simulation result shows that the proposed binarization method achieves performance improvement interms of F-measure, NRM, MPM, PSNR as 88.4461, 0.0708, 0.00265 and 18.420 respectively.
机译:由于各种形式的降级,从降级的历史文档中获取信息一直是一项艰巨的任务。图像二值化对于还原降级的历史文档非常重要。尽管已经提出了许多算法,但是仍然需要一种有效的算法来解决各种退化问题。本文提出了一种优化的二值化技术,通过结合局部图像对比度和梯度的方法来解决这些问题。首先对输入图像进行二值化处理,然后使用canny egde贴图提取文本笔触边缘像素。为进一步增强效果,基于形状进行形态学运算。最后对前景和背景像素进行自适应阈值确定。质量方面,该方法已在3个公共数据集(DIBCO 2009、2010、2011)上进行了测试,这些数据集来自模式识别和图像分析(PRImA)研究实验室。仿真结果表明,所提出的二值化方法在F值,NRM,MPM,PSNR方面分别达到了88.4461、0.0708、0.00265和18.420。

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