text: The course description provided by instructors is an important piece of information as it defines what is expected from the instructor and what he she is going to deliver during a particular course. One of the key components of a course description is the Learning Outcomes section. The contents of this section are used by program managers who are tasked to compare and match two different courses during the development of Transfer Agreements between different institutions. This research introduces the development of visual tools for understanding the two different courses and making comparisons. We designed methods to extract the text from a course description document, developed an algorithm to perform semantic analysis, and displayed the results in a web interface. We are able to achieve the intermediate results of the research which includes extracting, analyzing and visualizing the data.
  @cite_8: In recent years, PDF has become the de-facto standard for the exchange of print-oriented documents on the Web. This includes many business documents such as financial reports, newsletters and patent applications, and there are many commercial applications that require data to be extracted from these documents and processed by computer systems. A number of products currently exist on the market that navigate, extract and transform data from HTML pages; a process known as wrapping. One such methodology is Lixto, a product of research at our institute. However, none of these products are currently able to work with PDF files. We are investigating this possibility as part of the NEX-TWRAP project. This paper describes our work in progress, and details some of the low-level page segmentation techniques that we have investigated
summary: The extraction process focuses on segmentation process which involves dividing the document into blocks that are the smallest logical entity and then proceed with extraction in the later stages @cite_8 . The segmentation process is divided into sub parts, which include generating neighbourhood graph, creating page divisions and generating whitespace density graph. For our research, we need to detect headings to make sure the algorithm knows from which part of the document to extract the text. To make sure that the extracted text does not have document header footer or text from another columns(if document has multiple columns), detection of header footer text and multiple columns is also required. So, the most efficient way for our application is to detect the relevant headings and then analyze the area of the document which contains that heading before extracting the text. The authors have not seen such work before.