GDB: Gated convolutions-based Document Binarization

Understand

Document binarization is a key pre-processing step for many document analysis tasks.

• However, existing methods can not extract stroke edges finely, mainly due to the fair-treatment nature of vanilla convolutions and the extraction of stroke edges without adequate supervision by boundary-related information.
• In this paper, we formulate text extraction as the learning of gating values and propose an end-to-end gated convolutions-based network (GDB) to solve the problem of imprecise stroke edge extraction.
• The gated convolutions are applied to selectively extract the features of strokes with different attention.