An Evaluation of Table Detection Methods in Document Images

Document understanding is based on the analysis of individual document modalities (i.e., document tables, charts, diagrams and images). Document tables are of particular importance for the readers and convey important information about the contents and results of a study in close synergy with natural language text. Document analysis for the extraction of tabular information involves three steps, namely table detection, recognition and understanding. This paper presents a critical review of table detection methods with application document images. The published works are categorized into (a) rule-based approaches, (b) machine learning approaches and (c) hybrid approaches which leverage a combination of both aforementioned techniques. For each category of methods, a reference diagram showing the key processing tasks and their interaction is presented and discussed. The most representative methodologies of each category are tested and evaluated in terms of a set of features associated main with their level of maturity and performance.