Abstract: Efficient environment monitoring and information analysis in urban scenes has become one of primary tasks of smart cities. In smart cities, the recognition of text information in scene images, especially the extraction of Chinese text in scene images, is an intuitive and efficient method for analyzing scene information. However, the Chinese text extraction of the current scene images fails to achieve good results because of the uneven illumination and blurred images. In addition, the complexity of Chinese character structure is also an important factor affecting the Chinese text extraction. In order to solve this problem, this paper proposes an edge enhanced maximally stable extremal regions (MSER) detection method, which can extract the MSER under the conditions of illumination and blurring influence, and the non-MSER can be efficiently filtered by geometric feature constraints to obtain high quality candidate MSER. Then the proposed central aggregation is used to aggregate the candidate Chinese text field that has been divided into multiple MSER, so that the candidate region becomes a single candidate Chinese text component, and then these components are analyzed, and finally the correct Chinese text is selected by machine learning. Experiments show that the algorithm can extract Chinese text in natural scene images more effectively.

Key words: text extraction, maximally stable extremal regions (MSER), Chinese aggregation, support vector machine (SVM), Internet of things (IoT)