Table Detection in Handwritten Chemistry Documents Using Conditional Random Fields

In this paper, we present a new approach using conditional random fields (CRFs) to localize tabular components in an unconstrained handwritten compound document. Given a line-segmented document, the extraction of table is considered as a labeling task that consists in assigning a label to each line: Table Row label for a line which belongs to a table and Line Text label for a line which belongs to a text block. To perform the labeling task, we use a CRF model to combine two classifiers: a local classifier which assigns a label to the line based on local features and a contextual classifier which uses features taking into account the neighborhood. The CRF model gives the global conditional probability of a given labeling of the line considering the outputs of the two classifiers. A set of chemistry documents is used for the evaluation of this approach. The obtained results are around 88% of table lines correctly detected.