# Table Recognition for Sensitive Data Perception in an IoT Vision Environment
## 1 Introduction  
The Internet of Things (IoT) is a huge network consisting of various information-sensing devices on the Internet to connect people, machines, and objects at any time and any place [1]. The development of IoT technology allows us to perceive and understand the states and laws of the world, to measure, compute, and decide on various issues around us in a quantitative and intelligent way. It makes a twinned digital space corresponding to the physical world, which makes people's lives more convenient and intelligent. The deployment and application of IoT devices is the cornerstone of the IoT [1, 2]. Today, the number of intelligent IoT devices has exceeded 4 billion [3]. These IoT devices are generally embedded in sensors, and scattered throughout the physical world, to be used to sense the external environment and collect data. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, while these intelligent IoT devices have made our lives more intelligent and convenient, a series of data security issues have arisen [4; 5; 6; 7]. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of HD security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. At present, there are some ways to prevent sensitive data from leaking by using intelligent IoT vision devices. For example, optical character recognition (OCR)-based packet filtering reduces the risk of sensitive data spreading through the IoT. However, such methods do not work well for sensitive data presented in table form [8; 9]. For plaintext documents, the contents of words or sentences in a captured image are indeed of users' interest. However, in a table document, for a more accurate and sensitive data perception, we should extract the table structure, and obtain the data in every table cell, because each table cell represents a different meaning. In particular regarding financial data, a set of numbers recognized by OCR is meaningless and useless: only after the table structure is recognized can we know what the numbers represent. However, table images captured by the intelligent IoT vision devices face the issue of perspective transformation, which causes table-frame lines to be tilted, and the original parallel relationships and vertical relationships of the table frame lines to be broken, as shown in Figure 1. In addition, there are circle stamps and irregular handwritten signatures that may also exist in the table. These have a bad effect on the results of OCR. Therefore, a table-recognition algorithm based on the ruling-line feature is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. A table frame is made up of some vertical and horizontal ruling lines. The table frame can be detected while the ruling lines are detected. In this paper, a novel image structure element named \"Directional Connected Chain (DCC)\" is used to detect diagonal lines (See in Section 3.1). Then, the algorithm (see Section 3) based on DCC is proposed for recognition of the table by intelligent IoT vision device images. Table recognition lacks an evaluation metric at present. Most research incorporates accuracy and recall, which regard all table cells and error types as the same, as evaluation metrics, but this is unreasonable. An evaluation metric that takes table-cell importance degree and error types into consideration is put forward in this paper. A dataset [10] labeled with table-cell importance degree is also proposed, and classified into nine categories according to perspective transformation angles. Figure 1: Table with perspective transformation and interferences. To evaluate our algorithm, a prototype system is implemented. Experiments are conducted based on the benchmark and dataset proposed.