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Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection | IEEE Conference Publication | IEEE Xplore

Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection

Publisher: IEEE

Abstract:

A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embe...View more

Abstract:

A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embedding based text instance grouping algorithm is proposed to solve this problem. To learn the feature space, a TIEM (Text Instance Embedding Module) is trained to minimize the within instance scatter and maximize the between instance scatter. Similarity between different text instances are measured in the feature space and merged if they meet certain conditions. Experimental results show that our approach can effectively connect text regions that belong to the same text instance. Competitive performance of our approach has been achieved on CTW1500, Total-Text, IC15 and a subset consists of texts selected from the three datasets, with large spacing and occlusions.
Date of Conference: 10-15 January 2021
Date Added to IEEE Xplore: 05 May 2021
ISBN Information:
Print on Demand(PoD) ISSN: 1051-4651
Publisher: IEEE
Conference Location: Milan, Italy

References

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