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Structure Recognition System

Tables are an easy way to represent information in structural form. Table recognition is important for the extraction of such information from document images. Usually, modern OCR systems provide textual information coming from tables without recognizing actual table structure. However, recognition of physical structure of tables is required to get the logical meanings of the contents. Table structure recognition in heterogeneous documents is challenging due to variety of table layouts. It becomes even harder where no physical rulings are present in a table. This work proposes a novel learning based methodology for the recognition of table contents in heterogeneous document images. Text parts of the documents are classified as table or non-table elements using a pre-trained neural network model. Output of the neural network is further enhanced after applying a contextual post processing on each element to correct the classifications errors if any. The system is trained using a subset of UNLV and UW3 document images and showed more than 97% accuracy on a test set in detection of table and non-table elements.