4 Z. Tian, W. Huang, T. He, P. He and Y. Qiao
in [8] on the ICDAR 2013, and 0.61 F-measure over 0.54 in [35] on the ICDAR
2015). Furthermore, it is computationally efficient, resulting in a 0.14s/image
running time (on the ICDAR 2013) by using the very deep VGG16 model [27].
2 Related Work
Text detection. Past works in scene text detection have been dominated by
bottom-up approaches which are generally built on stroke or character detection.
They can be roughly grouped into two categories, connected-components (CCs)
based approaches and sliding-window based methods. The CCs based approaches
discriminate text and non-text pixels by using a fast filter, and then text pix-
els are greedily grouped into stroke or character candidates, by using low-level
properties, e.g., intensity, color, gradient, etc. [33,14,32,13,3]. The sliding-window
based methods detect character candidates by densely moving a multi-scale win-
dow through an image. The character or non-character window is discriminated
by a pre-trained classifier, by using manually-designed features [28,29], or recent
CNN features [16]. However, both groups of methods commonly suffer from poor
performance of character detection, causing accumulated errors in following com-
ponent filtering and text line construction steps. Furthermore, robustly filtering
out non-character components or confidently verifying detected text lines are
even difficult themselves [1,33,14]. Another limitation is that the sliding-window
methods are computationally expensive, by running a classifier on a huge number
of the sliding windows.
Object detection. Convolutional Neural Networks (CNN) have recently
advanced general object detection substantially [25,5,6]. A common strategy
is to generate a number of object proposals by employing inexpensive low-level
features, and then a strong CNN classifier is applied to further classify and refine
the generated proposals. Selective Search (SS) [4] which generates class-agnostic
object proposals, is one of the most popular methods applied in recent leading
object detection systems, such as Region CNN (R-CNN) [6] and its extensions [5].
Recently, Ren et al. [25] proposed a Faster R-CNN system for object detection.
They proposed a Region Proposal Network (RPN) that generates high-quality
class-agnostic object proposals directly from the convolutional feature maps. The
RPN is fast by sharing convolutional computation. However, the RPN proposals
are not discriminative, and require a further refinement and classification by an
additional costly CNN model, e.g., the Fast R-CNN model [5]. More importantly,
text is different significantly from general objects, making it difficult to directly
apply general object detection system to this highly domain-specific task.
3 Connectionist Text Proposal Network
This section presents details of the Connectionist Text Proposal Network (CTPN).
It includes three key contributions that make it reliable and accurate for text
localization: detecting text in fine-scale proposals, recurrent connectionist text
proposals, and side-refinement.
剩余15页未读，继续阅读
u010092512
- 粉丝: 0
- 资源: 11
我的内容管理
展开
最新资源
- 多模态联合稀疏表示在视频目标跟踪中的应用
- Kubernetes资源管控与Gardener开源软件实践解析
- MPI集群监控与负载平衡策略
- 自动化PHP安全漏洞检测：静态代码分析与数据流方法
- 青苔数据CEO程永：技术生态与阿里云开放创新
- 制造业转型： HyperX引领企业上云策略
- 赵维五分享：航空工业电子采购上云实战与运维策略
- 单片机控制的LED点阵显示屏设计及其实现
- 驻云科技李俊涛：AI驱动的云上服务新趋势与挑战
- 6LoWPAN物联网边界路由器：设计与实现
- 猩便利工程师仲小玉：Terraform云资源管理最佳实践与团队协作
- 类差分度改进的互信息特征选择提升文本分类性能
- VERITAS与阿里云合作的混合云转型与数据保护方案
- 云制造中的生产线仿真模型设计与虚拟化研究
- 汪洋在PostgresChina2018分享：高可用 PostgreSQL 工具与架构设计
- 2018 PostgresChina大会：阿里云时空引擎Ganos在PostgreSQL中的创新应用与多模型存储
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
