AON: Towards Arbitrarily-Oriented Text Recognition
arXiv:1711.04226
Abstract
Recognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.
Accepted by CVPR2018
References in corpus (12)
- Sequence to Sequence Learning with Neural Networks
- ADADELTA: An Adaptive Learning Rate Method
- Deep Residual Learning for Image Recognition
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
- Focusing Attention: Towards Accurate Text Recognition in Natural Images
- R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
- Synthetic Data for Text Localisation in Natural Images
- Deep Structured Output Learning for Unconstrained Text Recognition
- Multi-Oriented Text Detection with Fully Convolutional Networks
- Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
- Detecting Oriented Text in Natural Images by Linking Segments