基于深度学习的文本检测算法在银行运维中应用

Application of Text Detection Based on Deep Learning Algorithm in Operation and Maintenance of Bank

银行故障单中故障的截图常存在与自然场景中,能够在该图中精确地进行文本检测,将可以提高文本识别的精确度,并提高案例库的搜索和主动运维能力.为了提高自然场景文本检测的效率,提出了一种基于深度学习的自然场景文本检测算法.算法首先提取出图像中的最大稳定极值区域作为候选字母,利用单链接层次聚类得到候选文本,对候选文本进行中值滤波,最后通过一个深度置信网络DBN来删除非文本候选.实验结果表明,基于DBN的方法能有效提高自然场景文本检测的准确率,比传统方法具有更好的结果.

Screenshots of bank fault often exit in natural scenes. If the text can be accurately detected in the screenshots, it will be able to improve the accuracy of text recognition and improve the case base search and active operation and maintenance capabilities. In order to improve the efficiency of the text detection of natural scenes, an algorithm based on deep learning in natural scene is proposed. Firstly, candidate letters are extracted from the maximum stable extreme region, and candidate texts are generated by single-link hierarchical clustering, then the algorithm makes median filter for the candidate text. Lastly, non-texts are removed by the deep confidence network DBN. Experimental results show that DBN-based approach can effectively improve the accuracy of the text detection of natural scenes, and has better results than traditional methods.