# A Novel Approach to Data Extraction on Hyperlinked Webpages
## 2 Related Work
### Table and Header Detection  
Tables are often utilized to present information in a structured manner and numerous attempts were made in the literature to recognize and extract the data from tables. [PERSON] et al. [PERSON] et al. (2016) proposed a hidden Markov models (HMMs) and stochastic grammar-based method focusing on web table mining from the huge amount of unstructured hypertext documents. They introduced a method for table separation and recognition. They also interpreted and presented tables. The table mining task was performed in modules as shown in Figure 1.  
There were some limitations in HMMs and stochastic grammar that could negatively affect their performance, whereas CRF were shown to have several advantages over HM  
Figure 1: The flow of table mining modified from [PERSON] and [PERSON] (2002). [PERSON] et al. 's method [10] focuses on the header detection of different tables available in PDF documents collected from the dataset of CiteSeer [11]. Some techniques focused on table exploring by using table layout characteristics; however, table structure mattered a lot. Approximately, every different format proposed [12; 13] had its way of classifying tables available on the web. The header detection algorithm might be able to perform well if different categories of the table could be classified automatically. Sometimes, a table header tag, \(<\)th\(>\), contains both a column and row header due to which table header detection procedure is crucial, as shown in Table 1. Multiple levels are also noticeable in a given table header. [PERSON] [10] used heuristic methods and machine learning techniques for table header detection. This heuristic mechanism utilized the local minimum methods. Their random forest based classifier [14] achieved comparatively better performance than the other heuristic and machine learning techniques. To classify a table as real or non-real, the authors used an efficient and domain-independent machine learning-based table detection algorithm for web documents. Machine learning-based techniques are used to detect real tables, expressed in two-dimensional grid format, i.e., either in rows and columns having logical relations among cells, or in non-real format created for a specific layout for grouping data into condensed clusters as way to show information [15]. Table detection, structural analysis, and table interpretation are included by table understanding methods in web documents. [PERSON] [17] introduced a novel feature set that contained a total of 16 features to analyze the process of web pages. [PERSON] [4] identified approximately 150 million HTML tables on the web, mostly containing the structured and related data. [PERSON] et al. [14] has proposed a primary classification scheme to formalize the structured data (composed of an ordered set of x rows and y columns) into different notations of web tables (WT). Relational knowledge tables were further described in the secondary classification. The authors determined the overall candidate based on the relevant tables to expand the computational approach by presenting a general filtering-based approach they employed for scaling related table computation. Filtering conditions were used in two ways in the algorithm: fewer comparisons and fast comparisons [4; 15; 18]. The algorithm used tables to represent the data required formally, to get the reader's attention, or for informative comparisons [19]. Th scheme was focused on the basic understanding of the table structure and the identification of its cell components. Statistical relational learning (SRL) was employed to perform the task automatically [20]. To recognize critical cells, [PERSON] adpted the SRL algorithm [20; 21; 22], which is a probabilistic, query-based classifier that uses first-order logic as a representation language. [PERSON] adopted a selective, featurization approach to feature engineering that kept a feature construction phase with a feature selection process.