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.
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.
@cite_8
2140785769
This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips. The extracted text blocks obtained by horizontal projection are classified into three categories: small, average and large text blocks. After segmenting the large text blocks, the lines are obtained by matching adjacent blocks within two successive strips using spatial relationship. The document without overlapping or touching characters is segmented by making abstraction on the segmentation module of the large text blocks. The text line segmentation method has a 96 accuracy on a collection of 100 historical documents