Chinese text detection for natural scenes

JIA Ying; CHENG Yanyun

doi:10.19304/J.ISSN1000-7180.2021.0897

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Chinese text detection for natural scenes

更新时间：2026-09-04

- Chinese text detection for natural scenes
- Vol. 39, Issue 3, Pages: 25-33(2022)
- 作者机构：
  
  南京邮电大学机构自动化学院、人工智能学院,江苏,南京,210000
- 作者简介：
- 基金信息：
- DOI：10.19304/J.ISSN1000-7180.2021.0897
  CLC：
- Published：2022
- 稿件说明：
移动端阅览
贾颖, 程艳云. 面向自然场景的中文文本检测[J]. 微电子学与计算机, 2022, 39(3): 25-33.

JIA Ying, CHENG Yanyun. Chinese text detection for natural scenes[J]. 2022, 39(3): 25-33.
贾颖, 程艳云. 面向自然场景的中文文本检测[J]. 微电子学与计算机, 2022, 39(3): 25-33. DOI： 10.19304/J.ISSN1000-7180.2021.0897.

JIA Ying, CHENG Yanyun. Chinese text detection for natural scenes[J]. 2022, 39(3): 25-33. DOI： 10.19304/J.ISSN1000-7180.2021.0897.

摘要

随着深度学习的发展，自然场景的文本检测取得了进步，但多方向和弯曲中文文本检测效果仍不理想.针对多方向和弯曲中文文本的检测问题，提出一种融合注意力机制的多尺度文本检测方法.为了平衡模型准确性和降低计算复杂度，采用轻量级Resnet18为主干网络.针对特征金字塔(FPN)提取的特征分布不确定性的问题，嵌入平衡注意力机制(BAM)提取有效文本特征并抑制低效特征通道，进而提升检测方法的鲁棒性.针对空洞空间金字塔池化网络(ASPP)下采样时图像局部信息和细节信息丢失的问题，改进ASPP以降低特征图分辨率的损失.针对FPN提取特征不足以及感受野小的问题，将嵌入注意力机制的FPN和改进的ASPP并行增强特征提取融合.针对正负样本的不平衡性的问题，基于可微二值化模块在二值图损失中引入对数化的AC Loss，从而增强检测模型的泛化能力.在公开数据集MSRA-TD500上的实验结果表明，该算法与目前快速高效的DBnet相比，准确率、召回率和F值分别提升0.1%、1.4%和0.6%，并且该算法的检测速率也有较好表现.

Abstract

With the development of deep learning

the text detection of natural scenes has made progress

but the detection effect of multi-directional and curved Chinese text is still not ideal. A multi-scale text detection method isproposed

whichintegrates attention mechanism for the detection of multi-directional and curved Chinese text. In order to balance model accuracy and reduce computational complexity

a lightweight Resnet18 backbone network is adopted. Aiming at the problem of the uncertainty of the feature distribution extracted by the feature pyramid (FPN)

the embedded balanced attention mechanism (BAM) extracts effective text features and suppresses inefficient feature channels

thereby improving the robustness of the detection method. Aiming at the problem of the loss of image local information and detail information during downsampling of the Hollow Space Pyramid Pooling Network (ASPP)

ASPP is improved to reduce the loss of feature map resolution. Aiming at the problem of insufficient FPN feature extraction and small perception field

the FPN embedded in the attention mechanism and the improved ASPP parallel enhanced feature extraction are fused. Aiming at the problem of the imbalance of positive and negative samples

the logarithmic AC Loss is introduced into the binary graph loss based on the differentiable binarization module

thereby enhancing the generalization ability of the detection model. The experimental results on the public data set MSRA-TD500 show that compared with the current fast and efficient DBnet

the accuracy

recall and F value of this algorithm are increased by 0.1%

1.4% and 0.6% respectively

and the detection rate of this algorithm is also has a good performance.

关键词

Keywords

references

CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2): 266-277. DOI: 10.1109/83.902291.

LYU P, YAO C, WU W H, et al. Multi-oriented scene text detection via corner localization and region segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 7553-7563. DOI: 10.1109/CVPR.2018.00788.

LIU Y L, ZHANG S, JIN L W, et al. Omnidirectional scene text detection with sequential-free boxdiscretization[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Macao, China: IJCAI, 2019: 3052-3058. DOI: 10.24963/ijcai.2019/423.

LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multiBox detector[C]//Proceedings of the14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2.

TIAN Z, HUANG W L, HE T, et al. Detecting text in natural image with connectionist text proposal network[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 56-72. DOI: 10.1007/978-3-319-46484-8_4.

DENG D, LIU H F, LI X L, et al. PixelLink: detecting scene text via instance segmentation[C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI, 2018: 6773-6780.

BAEK Y, LEE B, HAN D, et al. Character region awareness for text detection[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 9357-9366. DOI: 10.1109/CVPR.2019.00959.

LIAO M H, SHI B G, BAI X, et al. Textboxes: a fast text detector with a single deep neural network[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI, 2017: 4161-4167.

HE K M, GKIOXARI G, DOLLAR P, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386-397. DOI: 10.1109/TPAMI.2018.2844175.

LIAO M H, WAN Z Y, YAO C, et al. Real-time scene text detection with differentiable Binarization[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI, 2020: 11474-11481. DOI: 10.1609/aaai.v34i07.6812.

LYU P, LIAO M H, YAO C H, et al. Mask TextSpotter: an end-to-end trainable neural network for spotting text with arbitrary shapes[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 71-88. DOI: 10.1007/978-3-030-01264-9_5.

SHI B G, BAI X, BELONGIE S. Detecting oriented text in natural images by linking segments[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 3482-3490. DOI: 10.1109/CVPR.2017.371.

ZHOU X Y, YAO C, WEN H, et al. EAST: an efficient and accurate scene text detector[C]//Proceeding of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 2642-2651. DOI: 10.1109/CVPR.2017.283.

LONG S B, RUAN J Q, ZHANG W J, et al. TextSnake: a flexible representation for detecting text of arbitrary shapes[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 19-35. DOI: 10.1007/978-3-030-01216-8_2.

WANG W H, XIE E Z, LI X, et al. Shape robust text detection with progressive scale expansion network[C]//Proceedings of 2019IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 9328-9337. DOI: 10.1109/CVPR.2019.00956.

WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 3-19. DOI: 10.1007/978-3-030-01234-2_1.

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