Evaluation of a Region Proposal Architecture for Multi-task Document Layout Analysis

Automatically recognizing the layout of handwritten documents is an important step towards useful extraction of information from those documents. The most common application is to feed downstream applications such as automatic text recognition and keyword spotting; however, the recognition of the layout also helps to establish relationships between elements in the document which allows to enrich the information that can be extracted. Most of the modern document layout analysis systems are designed to address only one part of the document layout problem, namely: baseline detection or region segmentation. In contrast, we evaluate the effectiveness of the Mask-RCNN architecture to address the problem of baseline detection and region segmentation in an integrated manner. We present experimental results on two handwritten text datasets and one handwritten music dataset. The analyzed architecture yields promising results, outperforming state-of-the-art techniques in all three datasets.

Multi-Task Handwritten Document Layout Analysis

Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, form the extraction of the text lines to the type of region where it belongs. We present a system based on artificial neural networks which is able to extract not only the baselines present in the document, but geometric and logic layout analysis of the document as well. Experiments in three different datasets demonstrate the potential of the method and show competitive results with state-of-the-art methods.