Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2201.04866v1
• Date: Thu, 13 Jan 2022 10:15:42 GMT
• Title: Weakly Supervised Scene Text Detection using Deep Reinforcement Learning
• Authors: Emanuel Metzenthin, Christian Bartz, Christoph Meinel
• Abstract summary: We propose a weak supervision method for scene text detection, which makes use of reinforcement learning (RL) The reward received by the RL agent is estimated by a neural network, instead of being inferred from ground-truth labels. We then use our proposed system in a weakly- and semi-supervised training on real-world data.
• Score: 6.918282834668529
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The challenging field of scene text detection requires complex data annotation, which is time-consuming and expensive. Techniques, such as weak supervision, can reduce the amount of data needed. In this paper we propose a weak supervision method for scene text detection, which makes use of reinforcement learning (RL). The reward received by the RL agent is estimated by a neural network, instead of being inferred from ground-truth labels. First, we enhance an existing supervised RL approach to text detection with several training optimizations, allowing us to close the performance gap to regression-based algorithms. We then use our proposed system in a weakly- and semi-supervised training on real-world data. Our results show that training in a weakly supervised setting is feasible. However, we find that using our model in a semi-supervised setting , e.g. when combining labeled synthetic data with unannotated real-world data, produces the best results.

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