To address the line/cell classication problem, we propose the Structure Detection in Verbose CSV Files (Strudel) approach, which is grounded on a multi-class random forest classifier. The following fiure shows the architecture of the approach. It first detects the dialect of a text file, and creates a verbose CSV file from it, based on the dialect. Then Strudel classifies first lines and then cells therein with the proposed feature sets. Cells of different types are distinguished by colors. We propose sophisticated features to model the individual classes for both classification tasks. The features can be categorized into three groups: 1) content features parsing the values of cells or lines, such as cell length and amount of words; 2) contextual features comparing the inspected cell or line with its neighbors, such as the similarity of data types between lines/cells; 3) computational features seeking to connect lines/cells with each other by inspecting arithmetic correlations between them.