| 摘要: |
| 复杂图像文字识别是基于内容图像检索的一个重要研究方向.针对图像中的文字可能存在倾斜、光照不均、噪音干扰和边缘柔化等多种异态问题，提出一种有效的异态汉字识别方法，称作SC-HOG.首先，利用稀疏编码得到基向量和稀疏系数，通过重构图像滤除噪音、处理边缘柔化；然后，利用梯度方向直方图抽取复原图像的汉字边缘梯度特征，削弱倾斜和光照的影响；最后，将获取的特征向量送入分类器，实现异态汉字的识别.通过合成数据集和真实数据集两方面的实验来验证SC-HOG方法的有效性：前一方面实验结果表明，SC-HOG方法对于倾斜、光照不均、噪音干扰和边缘柔化等异态情况有较强的鲁棒性；后一方面实验结果表明，SC-HOG方法在原生数字图像和场景图像真实样本集上也能取得较好的结果. |
| 关键词: 稀疏编码 梯度方向直方图 增量线性决策分析 汉字识别 文档图像处理 |
| DOI：10.13328/j.cnki.jos.004502 |
| 分类号: |
| 基金项目:国家自然科学基金（61201424）；天津市自然科学基金（12JCYBJC10100）；中央高校基本科研业务费专项资金（65012131） |
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| Research on Abnormal Chinese Character Recognition |
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WANG Kai, LI Cheng-Xue, WANG Qing-Ren, ZHAO Hong, ZHANG Jian
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College of Computer and Control Engineering, Nankai University, Tianjin 300071, China
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| Abstract: |
| Recognizing characters from the complex image plays an important role in content-based image retrieval and has been well studied in past decades. The methods for normal characters recognition, however, become inapplicable when characters suffer from skew, uneven illumination, noise and anti-aliasing. A new method, named SC-HOG, is proposed in this paper for recognizing abnormal Chinese characters. Firstly, sparse coding is applied on abnormal character image to smooth noises and reduce anti-aliasing. Secondly, HOG features that help reducing the influence of skew and uneven illumination are extracted. Finally, these features are fed into a well-trained classifier to recognize the character of the given image. Experiments on both synthetic and real data sets show that the proposed method, SC-HOG, achieves high accuracy on abnormal Chinese characters recognition. |
| Key words: sparse coding (SC) histograms of oriented gradients (HOG) incremental linear discriminant analysis (ILDA) Chinesecharacter recognition document image processing |
