Date post: | 15-Apr-2017 |
Category: |
Technology |
Upload: | andrea-pazienza |
View: | 155 times |
Download: | 0 times |
AN ABSTRACT ARGUMENTATION-BASEDSTRATEGY FOR READING ORDER DETECTION
IT@LIA 2015 - 1st AI*IA Workshop on
Intelligent Techniques At LIbraries and Archives
Stefano Ferilli and Andrea Pazienza
Tuesday 22th September 2015
Overview
1. Introduction
2. Document Processing: DoMInUS
3. Abstract Argumentation Framework
4. Argumentation-based Reading Order Detection
5. Evaluation
6. Conclusions
2
Introduction
Introduction
Document Image Analysis (DIA): branch of AutomaticDocument Processing that aims at extracting high-level
information from the low-level representation of a document.
Reading Order Detection (ROD): hot problem and new
approaches are needed to tackle di�cult cases.
# Use of an Abstract Argumentation Framework (AF) to
solve this problem.
4
Document Processing: DoMInUS
DoMInUS
DoMInUS (DOcument Management INtelligent Universal
System): intensive exploitation of intelligent techniques in each
step of document processing.
Any document can be progressively partitioned into a
hierarchy of abstract representations, called its layout structure.
Several techniques to extract the high-level geometrical
structure of a document.
6
DoMInUS
Input: Vectorial description of each document page in terms of
blocks.
Output: set of frames, de�ned as collections of basic blocks.
7
Abstract Argumentation Framework
Argumentation Basics
Abstract structure AF � 〈A,R〉 in which
# A represents a set of abstract arguments# R represents an attack binary relation on A between
arguments
Directed graph representation, in which each node representing
an argument and each edge representing an attack.
The objective is to determine which subset(s) of its nodes can be
justi�ed.
αβ
γ
δ
ε
Figure: Graph representation of an AF
9
Extension-based Semantics
The justi�cation state in an AF can be determined according to
suitable semantics.
# Determine which subset (extension-based semantics) ofAF’s nodes can be de�ned as ’justi�ed’.
Let S ⊆ A a subset of arguments
# con�ict-free, i.e. @α, β ∈ S s.t. αRβ# acceptable, i.e. ∃F : 2S → 2
Ss.t. F(S) = {α | α is defended by S}
Then
# S is admissible if S ⊆ F(S)
# S is a Complete Ext. if S � F(S)
# S is a Ground Ext. if S is the minimal Complete Ext. (w.r.t. ⊆)
# S is a Preferred Ext. if S is the maximal Complete Ext. (w.r.t. ⊆)
10
Extension-based Semantics
α β γ
δ
ε
Figure: Argumentation Framework Example
# Admissible sets : {∅, {α}, {β}, {β, δ}}# Complete Extensions : {∅, {α}, {β, δ}}# Preferred Extensions : {{α}, {β, δ}}# Ground Extension : {∅}
11
Argumentation-based ReadingOrder Detection
Argumentation-based Reading Order Detection
DoMInUS to obtain the layout structure:
# layout blocks labeled with their type of content,
# image blocks are ignored,
# separators allows to partition the page into independent
portions.
Basic and document-independent reading rules:
# horizontally or vertically adjacent components are
candidates to be read consequently,
# a component at the bottom of the page might be followed
by a component at the top of an adjacent column,
# a rightmost (resp., leftmost) component might be followed
by a leftmost (resp., rightmost) component in an adjacent
row.
13
Argumentation-based Reading Order Detection
Each pair of blocks (A, B) is translated into an argument
representing the claim “components A and B are to be read one
after the other in a document”.
Formally, we express this in First-Order Logic using predicate:
# next/2, as next(A,B) to express reading order,
# attacks/2, as attacks(next(A,B),next(A,C)) toexpress con�icts in Argumentation Framework settings.
Once the Argumentation Framework is build, it is su�cient to
compute an extension to return an acceptable reading order
solution.
14
Argumentation-based Reading Order Detection
The document page in Figure yields the following formal
description:
(0,3), (1,2), (3,1), (4,5), (4,6), (4,7),
(5,6), (5,9), (6,7), (8,0), (8,4), (8,9)15
Argumentation-based Reading Order Detection
The following attacks are automatically derived:
(4,5)-(4,6), (4,6)-(4,5), (4,6)-(4,7), (4,7)-(4,6), (4,5)-(4,7), (4,7)-(4,5),
(5,6)-(5,9), (5,9)-(5,6), (8,0)-(8,4), (8,4)-(8,0), (8,0)-(8,9), (8,9)-(8,0),
(8,4)-(8,9), (8,9)-(8,4), (4,6)-(5,6), (5,6)-(4,6), (4,7)-(6,7), (6,7)-(4,7),
(5,9)-(8,9), (8,9)-(5,9)
16
Argumentation-based Reading Order Detection
The correct reading order is the following:
(3,1), (1,2), (2,4), (4,5), (5,6), (6,7)
17
Evaluation
Evaluation
The proposed technique was tested on a dataset including 103
document pages of di�erent layout complexity.
The justi�ed set of arguments was determined using PreferredExtensions.
For each extension, the recallwas evaluated as the ratio of
correct next/2 items retrieved over next/2 items in the correct
order sequence
Paper Magazine Newspaper Overall
#Documents 43 40 20 103
#Blocks 16.60 11.42 48.20 20.73
#Arguments 24.13 9.20 44.45 22.98
#Attacks 61.67 14.20 164.3 63.17
#Preferred Ext. 6.65 6.83 5 053.85 986.76
Recall(%) 91.32 71.75 70.74 77.94
19
Evaluation
Figure: A very complex document in the dataset
Newspaper pages in the dataset have a signi�cant impact on
the overall complexity of the dataset.
20
Evaluation
Qualitative analysis:
# Very complex non-Manhattan layouts handling,
# Deal with any kind of document independently of the
language in which it is written.
The technique might fail:
# On papers, when header and footer are not separated by
lines;
# On magazines, when multi-line titles are across di�erent
backgrounds;
# On newspapers, when columns of the same article are
separated by lines.
These problems might be tackled by re�ning the rules toconsider additional layout information, such as spacing and
font size.
21
Conclusions
Conclusions
Automatic strategy for identifying the correct reading order
of a document page’s components based on Abstract
Argumentation Framework.
# Unsupervised technique.
# It works on any kind of document based only on general
assumptions about how humans behave when reading
documents.
# Experimental results show that it is very e�ective, alsocompared to previous solutions that have been proposed
in the literature.
# Qualitative analysis of the results suggested possible
directions for further improvement of the approach.
23