目的 文档图像检索过程中，传统的光学字符识别（OCR）技术因易受文档图像质量和字体等相关因素的影响，难以达到有效检索的目的。关键词识别技术作为OCR技术的替代方案，不需经过繁琐的OCR识别，可直接对关键词进行检索。本文针对Harris算法聚簇现象严重和运算速度慢等问题，在关键词识别技术的框架下提出了改进Harris的图像匹配算法。方法 基于Fast进行特征点检测，利用Harris进行特征描述，并采用非极大值抑制的方法，最后利用暴力匹配中的汉明距离进行特征的相似性度量，输出最终的匹配结果。结果 实验结果表明本文算法在特征提取上的时间为0.101 s，相对于原始Harris算法的0.664 s和SIFT算法的1.066 s，实时性方面有了明显提高，改善了原始算法的聚簇现象，并且在无噪声的情况下，准确率达到98%，高于Sift算法的90%，召回率达到87.5%，而且在固定均值，不断提高方差的高斯噪声条件下，与Sift算法相比，准确率也高于后者，取得了较好的实验效果。结论 本文提出的方法满足了快速、精确的查找需求，在印刷体图像的文档图像检索中有效提高了检索率，具有较好的实验效果。

Objective In the 21st century, the rapid development of Internet information provides great convenience for people's lifestyles, but people also must face information redundancy when they go online because most information is now in text. The existence of forms emphasizes the importance of accurately and efficiently obtaining the information that users need. Moreover, with the acceleration of informationization, the number of electronic documents has risen sharply, making the efficient and fast retrieval of document images further urgent. In document image retrieval, traditional optical character recognition (OCR) technology has difficulty in achieving effective retrieval due to the quality of document images and fonts. As an alternative to OCR technology, word recognition technology does not require cumbersome OCR recognition and can directly search for keywords to achieve good results. In keyword extraction, local feature extraction has a more detailed and accurate description than global feature extraction. In terms of corner detection, this paper focuses on the serious clustering and slow computing speed of Harris algorithm. Method In the framework of word-spotting technology, an improved Harris image matching algorithm is proposed, which is used for document image retrieval for the first time. First, the original Harris algorithm uses the Gaussian function to smooth the window in the feature point extraction process of the image. When calculating the corner response value R, the differential operator is used as the directional derivative to calculate the number of multiplication operations, resulting in many computational algorithms, slow operation, and other issues. Given the deficiencies of the original Harris algorithm, FAST algorithm is used in the detection of corner points. 1) The gray values of the center pixel and surrounding 16 pixels are compared using the formula in the radius 3 field. 2) To improve the detection speed, the 0th and 8th pixel points on the circumference are first detected, and the two points on the other diameters are sequentially detected. 3) A difference between the gray values of 12 consecutive points and the p-point exceeding the threshold indicates a corner point. 4) After obtaining the primary corner, the Harris algorithm is used to remove the pseudo corner. Second, the original Harris algorithm sorts and compares the local maximum of the corner response function, establishes the response and coordinate matrices, records the local maximum and response coordinates, and compares the global maximum. At this point, all corner points have been recorded, but a case wherein multiple corner points coexist in the domain of a corner point, namely, "clustering" phenomenon, is likely. To address the serious clustering problem of the Harris algorithm, a nonmaximum value suppression method is adopted, which essentially searches for the local maximum and suppresses nonmaximum elements. When detecting the diagonal points, the local maximum is sorted from large to small, the suppression radius is set, a new response function matrix is established, and the corner points are extracted by continuously reducing the radius, thereby effectively avoiding Harris corner clustering. 1) The value of the corner response function of all pixels in the graph is calculated, the local maximum is searched for, and the pixel of the local maximum is recorded. 2) The local maximum ordering matrix and corresponding coordinate matrix are established, and the local maxima are sorted from large to small. 3) The suppression radius is set to the local maximum, a new matrix of response functions is established, and the corner points are extracted by continuously reducing the radius. 4) Whether the local maximum value is the maximum value within the suppression radius, that is, the desired corner point, is judged; if the condition is satisfied, then the local maximum value is added to the response function matrix. 5) The radius reduction is continued to extract the corner points. If the number of corner points is preset, then the process ends. Otherwise, step 4) is repeated. Result Experimental results show that the accuracy rate is 98% and the recall rate is 87.5% without noise. Good experimental results are obtained under the condition of constant mean and continuous improvement of variance of Gaussian noise. Compared with the SIFT algorithm, the time is considerably improved, and the accuracy is increased. Conclusion Starting from the document image of the printed matter, FAST+Harris is used to search for keywords under the framework of keyword recognition technology. On the one hand, this method saves retrieval time and improves the real-time performance of the algorithm. On the other hand, it improves the aggregation of Harris. The cluster phenomenon improves the accuracy of corner detection. Compared with the SIFT algorithm, time is greatly improved, and good experimental results are achieved under the influence of different degrees of noise.