In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition.
In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitrary-oriented scene text with both high accuracy and efficiency in a single network forward pass.
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
SCENE text recognition has attracted great interest from the academia and the industry in recent years owing to its importance in a wide range of applications.
In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way.
It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.
Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500.
Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community.
SOTA for Scene Text Detection on IC15