Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription.
PanoFlow is applicable to any existing flow estimation method and benefit from the progress of narrow-FoV flow estimation.
In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving--CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI).
Meanwhile, several pre-trained models for the Chinese and English recognition are released, including a text detector (97K images are used), a direction classifier (600K images are used) as well as a text recognizer (17. 9M images are used).