2017-2018_OCR_papers

1. 简单背景

基于深度的OCR方法的发展历程

近年来OCR发展热点与趋势

检测方法按照主题进行分类

2. ECCV + CVPR + ICCV +AAAI

检测

  • 水平文本

    • Shangxuan Tian——【ICCV2017】WeText_Scene Text Detection under Weak Supervision
    • Shitala Prasad——【ECCV2018】Using Object Information for Spotting Text
    • XiangBai——【AAAI2017】TextBoxes_A Fast Text Detector with a Single Deep Neural Network
    • Sheng Zhang——【AAAI2018】Feature Enhancement Network_A Refined Scene Text Detector
  • 倾斜文本
    • ChengLin Liu——【ICCV2017】Deep Direct Regression for Multi-Oriented Scene Text Detection
    • Chuhui Xue——【ECCV2018】Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping
    • Cong Yao——【CVPR2017】EAST_An Efficient and Accurate Scene Text Detector
    • Dafang He——【CVPR2017】Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild
    • Dan Deng——【AAAI2018】PixelLink_Detecting Scene Text via Instance Segmentation
    • Fangfang Wang——【CVPR2018】Geometry-Aware Scene Text Detection With Instance Transformation Network
    • Han Hu——【ICCV2017】WordSup_Exploiting Word Annotations for Character based Text Detection
    • Lianwen Jin——【CVPR2017】Deep Matching Prior Network_Toward Tighter Multi-oriented Text Detection
    • Pan He——【ICCV2017】Single Shot Text Detector With Regional Attention
    • XiangBai——【CVPR2017】Detecting Oriented Text in Natural Images by link Segments
    • XiangBai——【CVPR2018】Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation
    • XiangBai——【CVPR2018】Rotation-Sensitive Regression for Oriented Scene Text Detection
    • Yingli Tian——【CVPR2017】Unambiguous Text Localization and Retrieval for Cluttered Scenes
    • Yue Wu——【ICCV2017】Self-Organized Text Detection With Minimal Post-Processing via Border Learning
    • Zichuang Liu——【CVPR2018】Learning Markov Clustering Networks for Scene Text Detection
  • 曲线文本
    • Shangbang Long——【ECCV2018】TextSnake_A Flexible Representation for Detecting Text of Arbitrary Shapes

识别

  • Wei Liu——【AAAI2018】Char-Net_A Character-Aware Neural Network for Distorted Scene Text Recognition
  • Yang Liu——【ECCV2018】Synthetically Supervised Feature Learning for Scene Text Recognition
  • Zhanzhan Cheng——【CVPR2018】AON Towards Arbitrarily-Oriented Text Recognition
  • Zhanzhan Cheng——【CVPR2018】Edit Probability for Scene Text Recognition
  • Zhanzhan Cheng——【ICCV2017】Focusing Attention_Towards Accurate Text Recognition in Natural Images
  • Zichuan Liu——【AAAI2018】SqueezedText_A Real-time Scene Text Recognition by Binary Convolutional

检测+识别

  • Christian Bartz——【AAAI2018】SEE_Towards Semi-Supervised End-to-End Scene Text Recognition
  • Chulmoo Kang——【AAAI2017】Detection and Recognition of Text Embedded in Online Images via Neural Context Models
  • Chunhua Shen——【ICCV2017】Towards End-to-end Text Spotting with Convolutional Recurrent
  • Fangneng Zhan——【ECCV2018】Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
  • Lluis Gomez——【ECCV2018】Single Shot Scene Text Retrieval
  • Lukas Neumann——【ICCV2017】Deep TextSpotter_An End-to-End Trainable Scene Text Localization and Recognition Framework
  • Weilin Huang——【CVPR2018】An End-to-End TextSpotter With Explicit Alignment and Attention
  • XiangBai——【ECCV2018】Mask TextSpotter An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
  • XiangBai——【PAMI2018】ASTER_An Attentional Scene Text Recognizer with Flexible Rectification
  • YuQiao——【CVPR2018】FOTS Fast Oriented Text Spotting With a Unified Network

3. 其他CV会议期刊

2017年

  • Daitao Xing——【2017】ArbiText_Arbitrary-Oriented Text Detection in Unconstrained Scene
  • Dena Bazazian——【2017】Improving Text Proposals for Scene Images with Fully Convolutional Networks
  • Fan Jiang——【2017】Deep Scene Text Detection with Connected Component Proposals
  • Jiaqi Ma——【2017】Arbitrary-Oriented Scene Text Detection via Rotation Proposals
  • Lluis Gomez——【PR2017】TextProposals_A text-specific selective search algorithm for word spotting in the wild
  • Siyang Qin——【2017】Cascaded Segmentation-Detection Networks for Word-Level TextSpotting
  • Suman Ghosh——【2017】R-PHOC_Segmentation-Free Word Spotting using CNN
  • Xiangyu Zhu——【ICDAR2017】Deep Residual Text Detection Network for Scene Text
  • Yingying Jiang——【2017】R2CNN_Rotational Region CNN for Orientation Robust Scene Text Detection
  • Yuchen Dai——【2017】Fused Text Segmentation Networks for Multi-Oriented Scene Text Detection
  • Yuliang Liu——【2017】Detecting Curve Text in the Wild_New Dataset and New Solution(曲线文本)

2018年

  • Chunhua Shen——【2018】Correlation Propagation Networks for Scene Text Detection
  • Dafang He——【2018】TextContourNet_a Flexible and Effective Framework for Improving Scene Text
  • Jun Du——【ICPR2018】Sliding Line Point Regression for Shape Robust Scene Text Detection
  • Qiangpeng Yang——【IJCAI2018】IncepText_A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection
  • QiYuan——【2018】A Single Shot Text Detector with Scale-adaptive Anchors
  • **XiangBai——【2018TIP】TextBoxes++_A Single-Shot Oriented Scene Text Detector**
  • XiangBai——【PAMI2018】ASTER_An Attentional Scene Text Recognizer with Flexible Rectification
  • XiangLi——【2018】Shape Robust Text Detection with Progressive Scale Expansion Network
  • Yu Qiao——【BMVC2018】Boosting up Scene Text Detectors with Guided CNN
  • Zhuoyao Zhong——【2018】An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches

参考文献

  1. He P, Huang W, He T, et al. Single shot text detector with regional attention[C]//The IEEE International Conference on Computer Vision (ICCV). 2017, 6(7).
  2. Liu Y, Jin L. Deep matching prior network: Toward tighter multi-oriented text detection[C]//Proc. CVPR. 2017: 3454-3461.
  3. Liao M, Shi B, Bai X. Textboxes++: A single-shot oriented scene text detector[J]. IEEE Transactions on Image Processing, 2018, 27(8): 3676-3690.
  4. He W, Zhang X Y, Yin F, et al. Deep direct regression for multi-oriented scene text detection[J]. arXiv preprint arXiv:1703.08289, 2017.
  5. Zhou X, Yao C, Wen H, et al. EAST: an efficient and accurate scene text detector[C]//Proc. CVPR. 2017: 2642-2651.
  6. Deng D, Liu H, Li X, et al. PixelLink: Detecting Scene Text via Instance Segmentation[J]. arXiv preprint arXiv:1801.01315, 2018.
  7. Hu H, Zhang C, Luo Y, et al. Wordsup: Exploiting word annotations for character based text detection[C]//Proc. ICCV. 2017.
  8. Tian S, Lu S, Li C. Wetext: Scene text detection under weak supervision[C]//Proc. ICCV. 2017.
  9. Xue C, Lu S, Zhan F. Accurate Scene Text Detection Through Border Semantics Awareness and Bootstrapping[C]//European Conference on Computer Vision. Springer, Cham, 2018: 370-387.
  10. Yuliang L, Lianwen J, Shuaitao Z, et al. Detecting curve text in the wild: New dataset and new solution[J]. arXiv preprint arXiv:1712.02170, 2017.
  11. Long S, Ruan J, Zhang W, et al. TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes[C]//European Conference on Computer Vision. Springer, Cham, 2018: 19-35.
  12. Lyu P, Yao C, Wu W, et al. Multi-oriented scene text detection via corner localization and region segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 7553-7563
  13. Prasad S, Kong A W K. Using Object Information for Spotting Text[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 540-557.
  14. Liao M, Zhu Z, Shi B, et al. Rotation-Sensitive Regression for Oriented Scene Text Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 5909-5918.
  15. Wang F, Zhao L, Li X, et al. Geometry-Aware Scene Text Detection With Instance Transformation Network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1381-1389.
  16. Zhang S, Liu Y, Jin L, et al. Feature Enhancement Network: A Refined Scene Text Detector[J]. arXiv preprint arXiv:1711.04249, 2017.

2017-2018_OCR_papers汇总的更多相关文章

  1. 2019年Unity学习资源指南[精心整理]

    前言 进入一个领域,最直接有效的方法就是,寻找相关综述性文章,首先你需要对你入门的领域有个概括性的了解,这些包括: 1.主流的学习社区与网站. 2.该领域的知名大牛与热心分享的从业者. 3.如何有效的 ...

  2. 【2017年新篇章】 .NET 面试题汇总(二)

    本次给大家介绍的是我收集以及自己个人保存一些.NET面试题第二篇 第一篇文章请到这里:[2017年新篇章] .NET 面试题汇总(一) 简介 此次包含的不止是.NET知识,也包含少许前端知识以及.ne ...

  3. 2017上半年技术文章集合【Android】—184篇文章分类汇总

    地址: http://blog.csdn.net/androidstarjack/article/details/77923753 声明 | 本文是于亚豪 原创 终端研发部 前言: 2017年已经过大 ...

  4. 这是一份很有诚意的2017 Google I/O大会的汇总 & 解析

    前言 在刚过去的凌晨(北京时间 5月18日 1.00-3.00),一年一度的2017年Google I/O大会在美国谷歌山景城海岸线圆形剧场如期举行 Google I/O 大会:Innovation ...

  5. 2017春季 JMU 1414软工助教 链接汇总

    助教自我介绍 学生博客链接和coding链接 [1414软工助教]团队博客汇总 助教总结 评分 个人作业1:四则运算控制台 结对项目1:GUI 个人作业2:案例分析 结对项目2:单元测试 团队作业1: ...

  6. 2017秋 FZU SDN 课程作业汇总

    课程: SDN课程上机作业:SDN上机作业 参考作业: deepYY SDN作业: SDN作业 faberry的博客:faberry peiqiaoWang的博客:peiqiaoWang 相关博客汇总 ...

  7. DLRS(深度学习应用于推荐系统论文汇总--2017年8月整理)

    Recommender Systems with Deep Learning Alessandro:ADAAlessandro Suglia, Claudio Greco, Cataldo Musto ...

  8. [转载]【BlackHat 2017】美国黑客大会首日议题汇总,演讲PPT下载也在这里

    今年是 Black Hat 举办的第 20 个年头,高温酷暑也挡不住全世界黑客和安全人员奔赴拉斯维加斯的热情.毕竟这可是一年一度的盛大狂欢啊.今年的 BHUSA 从美国东部时间时间 7 月 22 日( ...

  9. 【2017年新篇章】 .NET 面试题汇总(一)

    开篇 本次给大家介绍的是我收集以及自己个人保存一些.NET面试题 简介 此次包含的不止是.NET知识,也包含少许前端知识以及.net面试时所涉及的种种考点,希望能给找工作的同学们哪怕一点点帮助. 古人 ...

  10. 2017年php面试题汇总

    1.http状态码 200 这个没有什么好说的,是代表请求被正常的处理成功了 302 代表临时重定向 400 400表示请求报文中存在语法错误.需要修改后再次发送 403 表明请求访问的资源被拒绝了. ...

随机推荐

  1. C# 集合已修改 可能无法执行枚举操作 zz

    今天编写程序时 修改了list集合 在foreach时报 “集合已修改:可能无法执行枚举操作.”错误. 首先想到的是没有锁定集合对象. 增加了 private readonly object sync ...

  2. 动态规划——Best Time to Buy and Sell Stock III

    题意:用一个数组表示股票每天的价格,数组的第i个数表示股票在第i天的价格. 如果最多进行两次交易,但必须在买进一只股票前清空手中的股票,求最大的收益. 示例 1:Input: [3,3,5,0,0,3 ...

  3. 最近公共祖先(LCA)的三种求解方法

    转载来自:https://blog.andrewei.info/2015/10/08/e6-9c-80-e8-bf-91-e5-85-ac-e5-85-b1-e7-a5-96-e5-85-88lca- ...

  4. S0.0 计算机如何看东西

    标签(空格分隔):数字图像处理 opencv 当我们用特定软件打开一张图片或者更改某些位图的格式为txt时,就会发现图像的本质不过就是一堆数据罢了. 采样 我们可以用相机采样到一幅二维图像,图像的分辨 ...

  5. HTML入门7

    这篇来些可能用的比较少的,调试HTML 程序员调试代码常见,遇到问题一切正常,找出问题解决,满足 来了解下HTML调试, 在浏览器解析和显示之前HTML不会被编译成其他形式,只是解析而不是编译因此运行 ...

  6. 问题11:web前端开发规范手册(转)

    一.规范目的 1.1  概述 ..................................................................................... ...

  7. Nestjs Graphql

    文档 工作示例 安装依赖: npm i --save @nestjs/graphql apollo-server-express graphql-tools graphql app.module.ts ...

  8. linux学习:特殊符号,数学运算,图像与数组与部分终端命令用法整理

    指令:let.expr.array.convert.tput.date.read.md5.ln.apt.系统信息 一:特殊符号用法整理 系统变量 $# 是传给脚本的参数个数 $0 是脚本本身的名字 $ ...

  9. java学习(五)--- 方法

    方法的定义 修饰符 返回值类型 方法名(参数类型 参数名){ ... 方法体 ... return 返回值; } 注意:非void方法必须有返回值 方法重载: 可以声明方法相同,但是参数类型不同的方法 ...

  10. Oracle 迁移某用户的数据到Sql Server

    准备条件: 1.Oracle11g数据库 2.Sql Server 2008 3.Microsoft SQL Server Migration Assistant for Oracle 步奏如下: 1 ...