【摘要】 文本区域的字符存在着不同的颜色极性.为了能够正确地把文本区域的灰度图像转换成OCR识别软件可以识别的二值图像,提出了一种判断文本区域字符颜色极性的方法.首先计算文本区域的灰度-梯度共生矩阵,并根据目标函数快速地找到分割的灰度和梯度最佳阈值;然后在此基础上提取特征向量,送入神经网络进行分类;最后根据颜色极性判断的结果,分割出字符.实验结果表明,提出的方法在复杂度不同的背景下,正确地识别出了不同类别的字符颜色极性.更多还原

【Abstract】 Characters in a text region may have different color polarities.To convert correctly the image with grayscale in an accepted text region into the OCR-ready binary image,a method is proposed to classify then recognize the color polarity of characters in a text region.The gray-gradient co-occurrence matrix of the text region is calculated,and the optimum thresholds of segmented grayscale and gradient are found quickly according to the objective function.Then,the feature vector is extracted from the gray-gradient co-occurrence matrix and fed into neural network to classify the color polarity.All the characters in the text region are finally segmented according to the classification of color polarities. Experimental results showed that the proposed method can recognize correctly different color polarities of characters in the background with different complexities.更多还原