Abstract

Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of documents, and moving towards machine learning methods that learn to solve tasks from examples of input/output pairs. Within machine learning, a particular class of models, known as deep learning models, have established themselves as the state-of-the-art for many image-based applications, including DIA. While traditional machine learning models typically operate on features designed by researchers, deep learning models are able to learn task-specific features directly from raw pixel inputs.This dissertation is collection of papers that proposes several deep learning models to solve a variety of tasks within DIA. The first task is historical document binarization, where an input image of a degraded historical document is converted to a bi-tonal image to separate foreground text from background regions. The next part of the dissertation considers document segmentation problems, including identifying the boundary between the document page and its background, as well as segmenting an image of a data table into rows, columns, and cells. Finally, a variety of deep models are proposed to solve recognition tasks. These tasks include whole document image classification, identifying the font of a given piece of text, and transcribing handwritten text in low-resource languages.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science