Feature Enhancement Network: A Refined Scene Text Detector
This work addresses scene text detection, particularly for smaller text, which is important for applications like document analysis and image understanding, but it appears incremental as it builds on existing region proposal and refinement methods.
The paper tackled the problem of detecting small scene text by proposing a Feature Enhancement Network (FEN) with task-specific feature fusion and an adaptively weighted pooling layer, achieving state-of-the-art results on ICDAR benchmarks with improved F-measure.
In this paper, we propose a refined scene text detector with a \textit{novel} Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with \textit{only} $3\times 3$ sliding-window feature and text detection refinement with \textit{single scale} high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with \textit{task-specific}, \textit{low} and \textit{high} level semantic features fusion to improve the performance of text detection. Besides, since \textit{unitary} position-sensitive RoI pooling in general object detection is unreasonable for variable text regions, an \textit{adaptively weighted} position-sensitive RoI pooling layer is devised for further enhancing the detecting accuracy. To tackle the \textit{sample-imbalance} problem during the refinement stage, we also propose an effective \textit{positives mining} strategy for efficiently training our network. Experiments on ICDAR 2011 and 2013 robust text detection benchmarks demonstrate that our method can achieve state-of-the-art results, outperforming all reported methods in terms of F-measure.