Zhanzhan Cheng, Yangliu Xu, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou
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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Scene Parsing | ICDAR2015 | Accuracy | 73 | AON |
| Scene Parsing | ICDAR 2003 | Accuracy | 91.5 | AON |
| 2D Semantic Segmentation | ICDAR2015 | Accuracy | 73 | AON |
| 2D Semantic Segmentation | ICDAR 2003 | Accuracy | 91.5 | AON |
| Scene Text Recognition | ICDAR2015 | Accuracy | 73 | AON |
| Scene Text Recognition | ICDAR 2003 | Accuracy | 91.5 | AON |