Single Shot Text Detector with Regional Attention

He, Pan; Huang, Weilin; He, Tong; Zhu, Qile; Qiao, Yu; Li, Xin

doi:10.1109/iccv.2017.331

Cited by 306 publications

(144 citation statements)

References 33 publications

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“…In [41], EAST was introduced by exploring IOU loss [39] to detect multioriented text instances (e.g., words), with impressive results achieved. Recently, a single-shot text detector (SSTD) [9] was proposed by extending SSD object detector [22] to text detection. SSTD encodes text regional attention into convolutional features to enhance text information.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Character Networks

Xing

Tian

Huang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

173

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Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful onestage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-theart approaches [25,24] by a large margin, e.g., with improvements of 65.33%→71.08% (with generic lexicon) on ICDAR 2015, and 54.0%→69.23% on Total-Text, on endto-end text recognition. Code is available at: https:// github.com/MalongTech/research-charnet.

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Section: Related Workmentioning

confidence: 99%

Convolutional Character Networks

Xing

Tian

Huang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

173

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“…Scene text detection has attracted much attention in the computer vision field because of its numerous applications, such as instant translation, image retrieval, scene parsing, geo-location, and blind-navigation. Recently, scene text detectors based on deep learning have shown promising performance [8,40,21,4,11,10,12,13,17,24,25,32,26]. These methods mainly train their networks to localize wordlevel bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

Character Region Awareness for Text Detection

Baek

Lee

Han

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

803

461

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Scene text detection methods based on neural networks have emerged recently and have shown promising results. Previous methods trained with rigid word-level bounding boxes exhibit limitations in representing the text region in an arbitrary shape. In this paper, we propose a new scene text detection method to effectively detect text area by exploring each character and affinity between characters. To overcome the lack of individual character level annotations, our proposed framework exploits both the given characterlevel annotations for synthetic images and the estimated character-level ground-truths for real images acquired by the learned interim model. In order to estimate affinity between characters, the network is trained with the newly proposed representation for affinity. Extensive experiments on six benchmarks, including the TotalText and CTW-1500 datasets which contain highly curved texts in natural images, demonstrate that our character-level text detection significantly outperforms the state-of-the-art detectors. According to the results, our proposed method guarantees high flexibility in detecting complicated scene text images, such as arbitrarily-oriented, curved, or deformed texts.

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“…To evaluate the adaptability of our learned anchor, we still set the input size to 800 pixels. As shown in Table.4, our method achieves an F-measure of 0.833, which also surpasses all of the anchorbased methods [22,32,9,24,16], including one-stage and two-stage frameworks. Compared with other approaches [38,21] which utilize a deep regression network that directly predict text region, our method still keep an absolute lead in running speed.…”

Section: Comparison To State Of the Artmentioning

confidence: 76%

STELA: A Real-Time Scene Text Detector With Learned Anchor

Deng

Gong

et al. 2019

IEEE Access

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To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in scene text detection tasks. In this work, we present a simple and intuitive method for multi-oriented text detection where each location of feature maps only associates with one reference box. The idea is inspired from the twostage R-CNN framework that can estimate the location of objects with any shape by using learned proposals. The aim of our method is to integrate this mechanism into a onestage detector and employ the learned anchor which is obtained through a regression operation to replace the original one into the final predictions. Based on RetinaNet, our method achieves competitive performances on several public benchmarks with a totally real-time efficiency (26.5f ps at 800p), which surpasses all of anchor-based scene text detectors. In addition, with less attention on anchor design, we believe our method is easy to be applied on other analogous detection tasks. The code will publicly available at https://github.com/xhzdeng/stela.

show abstract

Single Shot Text Detector with Regional Attention

Cited by 306 publications

References 33 publications

Convolutional Character Networks

Convolutional Character Networks

Character Region Awareness for Text Detection

STELA: A Real-Time Scene Text Detector With Learned Anchor

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