Natural Scene Text Detection Based on MSER
Kun Wang, Guokuan Li, Xujun Liu, Jingkun Yan, Shuli Li, Hao Huang · 2018
Scene text detection has important applications in the fields of intelligent transportation, industrial automation, multimedia retrieval and so on.This paper employs the improved MSER algorithm combined with convolutional neural network for scene text detection.Gradient amplitude enhancement processing is used to enhance the text boundary before a combinational suppression strategy is applied to filter out coincident regions, approximately coincident regions and nested regions.Then the Char-CNN classifier is designed to classify the candidate regions.A hierarchical clustering algorithm is used to merge the candidate regions, and finally generate the text position information.We evaluate the algorithm performance on the ICDAR2013 dataset.The results show that the improved MSER algorithm increases the recall rate of the character region from 88.9% to 90.2%, and the proportion of character regions in the candidate regions increases from 3.25% to 35.19%.And the classification accuracy of Char-CNN is 93.6%.The recall and accuracy rate of the algorithm are 0.68 and 0.85 respectively, and the F-Measure value is 0.76.Compared with existing scene text detection algorithms, the proposed algorithm has a competitive overall performance.