科研之友
微信
新浪微博
Facebook
分享链接
摘要
Optimal data access and massive data derived information extraction has become an essential technology nowadays. Table-related paradigm is a kind of efficient structure for the clustered data designation, display and analysis. It has been widely used on Internet and vertical fields due to its simplicity and intuitiveness. Computer based tables, pictures or portable document format(PDF) files as the carrier will cause structural information loss. It is challenged to trace the original tables back. Inefficient manual based input has more errors. Therefore, two decadal researches have focused on the computer automatic recognition of tables issues originated from documents or PDF files and multiple tasks loop. To obtain the table structure and content and extract specific information, table recognition aims to detect the table via the image or PDF and other electronic files automatically. It is composed of three tasks recognition types like table area detection, table structure recognition and table content recognition. There are two types of existed table recognition methods in common. One is based on optical character recognition (OCR) technology to recognize the characters in the table directly, and then analyze and identify the position of the characters. The other one is to obtain the key intersections and the positions of each frameline of the table through digital image processing to analyze the relationship between cells in the table. However, most of these methods are only applicable to a single field and have poor generalization ability. At the same time, it is constrained of some experience-based threshold design. Thanks to the development of deep learning technology, semantic segmentation algorithm, object detection algorithm, text sequence generation algorithm, pre training model and related technologies facilitates technical problem solving for table recognition. Most deep learning algorithms have carried out adaptive transformation according to the characteristics of tables, which can improve the effect of table recognition. It uses object detection algorithm for table detection task. Object detection and text sequence generation algorithms are mainly used for table structure recognition. Most pre training models have played a good effect on the aspect of table content recognition. But many table structure recognition algorithms still cannot handle these well for wireless tables and less line tables. On the aspects of table images of natural scenes, the relevant algorithms have challenged to achieve the annotation in practice due to the influence of brightness and inclination. A large number of datasets provide sufficient data support for the training of table recognition model and improve the effect of the model currently. However, there are some challenging issues between these datasets multiple annotation formats and different evaluation indicators. Some datasets provide the hyper text markup language(HTML) code of the structure only in the field of table structure recognition and some datasets provide the location of cells in the table and the corresponding row and column attributes. Some datasets are based on the position of cells or the content of cells in accordance with evaluation indicators. Some datasets are based on the adjacent relationship between cells or the editing distance between HTML codes for the recognition of table structure. Our research critically reviews the research situation of three sub tasks like table detection, structure recognition and content recognition and try to predict future research direction further. ? 2022, Editorial Office of Journal of Image and Graphics. All right reserved.
- 单位
