Table Structure Extraction From Document Images

Abstract

Conventional computer vision applications are highly dependent on handcrafted features for classification and object detection problems. The recent development in the domain of deep learning has solved many complex problems with automatic features selection. However, deep learning algorithms are data-hungry and require a large amount of labeled data for training. Due to huge variations in the types and layouts of tables, table detection and structure extraction from document images has been remained an inter esting topic for many researchers for the past many decades but until today, there is no reliable solution available that can fit all layouts and types. In this paper, we present a heuristics-based approach that utilizes positional features to extract structure and information from invoice document images. Due to the nature of the problem, which includes table detection, table structure recognition, and information extraction, currently, there is no single deep learning model that detects, recognizes and extracts information from tables end to end. Also, there is no large labeled data set available for invoice images that can be utilized for the training of deep learning models. Due to this complexity, we propose that by building a heuristics and rules-based approach by thorough data analysis coupled with positional features of the word bounding boxes generated by OCR will create a foundation for reliable table structured recognition and information extraction. We proposed a single pipeline instead of three which includes image prepossessing, word bounding box extraction using OCR, table structure recognition, and information extraction. Table structure recognition and information extraction are coupled to extract reliable information from invoices.