Recognition of camera-captured low-quality characters using motion blur information
Recognition of camera-captured low-quality characters using motion blur information
复制标题
DOI：
10.1016/j.patcog.2008.01.009
复制
发表时间：
2008-07
期刊：
影响因子：
--
通讯作者：
H. Ishida;Tomokazu Takahashi;I. Ide;Y. Mekada;H. Murase
中科院分区：
文献类型：
--
作者：
H. Ishida;Tomokazu Takahashi;I. Ide;Y. Mekada;H. Murase
基于相机的字符识别随着配备相机的便携式设备的日益增长的使用而获得关注。手持相机识别字符的最具挑战性的问题之一是，由于手的振动，捕获的图像经历运动模糊。由于小字符的运动模糊很难通过图像恢复去除，我们提出了一种无需去模糊的识别方法。所提出的方法包括在训练步骤中的生成学习方法，通过控制模糊参数来模拟模糊图像。该方法包括两个步骤。第一步基于子空间方法识别模糊字符，第二步利用从摄像机运动估计的模糊参数对结构相似的字符进行重新分类。我们已经通过实验证明，运动模糊的有效使用提高了相机捕获字符的识别精度。
Camera-based character recognition has gained attention with the growing use of camera-equipped portable devices. One of the most challenging problems in recognizing characters with hand-held cameras is that captured images undergo motion blur due to the vibration of the hand. Since it is difficult to remove the motion blur from small characters via image restoration, we propose a recognition method without de-blurring. The proposed method includes a generative learning method in the training step to simulate blurred images by controlling blur parameters. The method consists of two steps. The first step recognizes the blurred characters based on the subspace method, and the second one reclassifies structurally similar characters using blur parameters estimated from the camera motion. We have experimentally proved that the effective use of motion blur improves the recognition accuracy of camera-captured characters.