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Text recognition is the key pillar for many real-world multimedia applications. Existing text recognition approaches focus on recognizing isolated instances, whose text fields are visually separated and have no interference with each other. Moreover, these approaches cannot handle overlapped instances that often appear in sheets like invoices, receipts and math exercises, where printed templates are generated beforehand and extra contents are added afterward on existing texts. In this paper, we aim to tackle this problem by proposing RecycleNet, which automatically extracts and reconstructs overlapped instances by fully recycling the intersecting pixels that used to be obstacles for recognition. RecycleNet parallels to existing recognition systems, and serves as a plug-and-play module to boost recognition performance with zero-effort. We also released an OverlapText-500 dataset, which helps to boost the design of better overlapped text recovery and recognition solutions. |
