Probabilistic Graphical Models for Document Analysis

November 15, 2016 at 11:00 am by

Place: CVC Sala d’actes

Dr. Laurence Likforman-Sulem, Telecom ParisTech, Paris, France
Dr. Simone Marinai, Università degli Studi di Firenze, Firenza, Italy
Dr. Josep Lladós, Universitat Autònoma de Barcelona, Barcelona, Spain

Thesis Supervisor:

Dr. Oriol Ramos Terrades, Computer Vision Center & Dep. of Computer Science, Universitat Autònoma de Barcelona, Spain.

In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on

several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems.


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