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.
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
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.
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.
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task.
It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text.
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.
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting.
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.
Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision.