Edit Probability for Scene Text Recognition

Understand

We consider the scene text recognition problem under the attention-based encoder-decoder framework, which is the state of the art.

• The existing methods usually employ a frame-wise maximal likelihood loss to optimize the models.
• When we train the model, the misalignment between the ground truth strings and the attention's output sequences of probability distribution, which is caused by missing or superfluous characters, will confuse and mislead the training process, and consequently make the training costly and degrade the recognition accuracy.
• To handle this problem, we propose a novel method called edit probability (EP) for scene text recognition.