Scene Text Recognition With Finer Grid Rectification

• URL: http://arxiv.org/abs/2001.09389v1
• Date: Sun, 26 Jan 2020 02:40:11 GMT
• Title: Scene Text Recognition With Finer Grid Rectification
• Authors: Gang Wang
• Abstract summary: This paper proposed an end-to-end trainable model consists of a finer rectification module and a bidirectional attentional recognition network(Firbarn) The results of extensive evaluation on the standard benchmarks show Firbarn outperforms previous works, especially on irregular datasets.
• Score: 6.598317412802175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene Text Recognition is a challenging problem because of irregular styles and various distortions. This paper proposed an end-to-end trainable model consists of a finer rectification module and a bidirectional attentional recognition network(Firbarn). The rectification module adopts finer grid to rectify the distorted input image and the bidirectional decoder contains only one decoding layer instead of two separated one. Firbarn can be trained in a weak supervised way, only requiring the scene text images and the corresponding word labels. With the flexible rectification and the novel bidirectional decoder, the results of extensive evaluation on the standard benchmarks show Firbarn outperforms previous works, especially on irregular datasets.

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