.png){kind=icon}
返回
C3E: A framework for chart classification and content extraction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2024.109861.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Incorporating charts into technical documents enhances richness by simplifying complex data representation and improving comprehension. However, automated chart content extraction (CCE) presents a significant challenge within the domain of document analysis and understanding. The CCE problem can be viewed through a series of six sub-tasks: chart classification (CC), text detection and recognition (TDR), text role classification (TRC), axis analysis, legend analysis, and data extraction. Improving these sub-tasks is important for enhancing the effectiveness of CCE. This paper introduces the chart classification and content extraction (C3E) framework, with a primary focus on the first three sub-tasks of CCE: CC, TDR, and TRC. We propose a ChartVision model for the CC, an EfficientNet-based model coupled with a dual- branch architecture incorporating a novel hybrid convolutional and dilated attention module. For text detection and TRC, we introduce a novel CCE method based on YOLOv5, CCE-YOLO, designed for localizing and classifying textual components of varying sizes. Further, for text recognition, we employ a convolutional recurrent neural network with connectionist temporal classification loss. We conducted experimental analysis on benchmark datasets to assess the effectiveness of our approach across each sub-task. Specifically, we evaluated CC, TDR, and TRC methods using the UB-PMC 2020 and UB-PMC 2022 datasets from the ICPR2020 and ICPR2022 CHART-Infographics competitions. The C3E framework achieved notable F1-scores of 94.26%, 92.44%, and 80.64% for CC, TDR, and TRC, respectively on the UB-PMC 2020 dataset and 94.0%, 91.98%, and 84.48% for CC, TDR, and TRC, respectively on the UB-PMC 2022 dataset.
Keyword:
Document understanding
Object detection
Chart infographics
Computer vision
BVIP
Chart classification
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
Assessment of neurological manifestations in hospitalized patients with COVID‐19Covid-19住院患者神经系统表现的评估
OAAI
OAAI分享收藏Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor AirOAAI
Indoor AirIF0
OAAI分享收藏Synthesis of n-type semiconducting diamond film using diphosphorus pentaoxide as the doping source以五氧化二磷为掺杂源合成n型半导体金刚石膜
PREAI
分享收藏FACTOR II AND X (FIIX) MONITORED WARFARIN IS MORE EFFECTIVE THAN PT-INR MONITORED WARFARIN AND DIRECT ORAL ANTICOAGULANTS IN PATIENTS ANTICOAGULATED LONG-TERM FOR NON-VALVULAR ATRIAL FIBRILLATION. THE GREATER REYKJAVIK ORAL ANTICOAGULATION (GROAC) STUDY因子II和X（FIIX）监测华法林比PT-INR监测华法林和直接口服抗凝剂在长期抗凝治疗非瓣膜性心房颤动患者中更有效。雷克雅未克口服抗凝研究（GROAC研究）。
PREAI
分享收藏Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization基于遗传算法和粒子群算法的节能机器人配置和运动规划Energies
OAAI
EnergiesIF0
OAAI分享收藏
查看更多内容