A Multi-Object Rectified Attention Network for Scene Text Recognition

Abstract

Irregular text is widely used. However, it is considerably difficult to recognize because of its various shapes and distorted patterns. In this paper, we thus propose a multi-object rectified attention network (MORAN) for general scene text recognition. The MORAN consists of a multi-object rectification network and an attention-based sequence recognition network. The multi-object rectification network is designed for rectifying images that contain irregular text. It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text. It is trained in a weak supervision way, thus requiring only images and corresponding text labels. The attention-based sequence recognition network focuses on target characters and sequentially outputs the predictions. Moreover, to improve the sensitivity of the attention-based sequence recognition network, a fractional pickup method is proposed for an attention-based decoder in the training phase. With the rectification mechanism, the MORAN can read both regular and irregular scene text. Extensive experiments on various benchmarks are conducted, which show that the MORAN achieves state-of-the-art performance. The source code is available.

9 Tables, 9 Figures. Accepted to appear in Pattern Recognition, 2019

References in corpus (7)

• Sequence to Sequence Learning with Neural Networks
• ADADELTA: An Adaptive Learning Rate Method
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Focusing Attention: Towards Accurate Text Recognition in Natural Images
• Detecting Curve Text in the Wild: New Dataset and New Solution
• Scene Text Recognition with Sliding Convolutional Character Models
• Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data