Authors:
James Pope
1
;
Daniel Powers
1
;
J. A. (Jim) Connell
1
;
Milad Jasemi
1
;
David Taylor
2
and
Xenofon Fafoutis
3
Affiliations:
1
Stephens College of Business, University of Montevallo, U.S.A.
;
2
University of Memphis, U.S.A.
;
3
DTU Compute, Technical University of Denmark, Denmark
Keyword(s):
Document Analysis, Supervised Machine Learning, Feature Selection, Optical Character Recognition.
Abstract:
Over the past three decades large amounts of information have been converted to image formats from paper
documents. Though in digital form, extracting the information, usually textual, from these documents requires
complex image processing and optical character recognition techniques. The processing pipeline from the
image to information typically includes an orientation correction task, document identification task, and text
analysis task. When there are many document variants the tasks become difficult requiring complex subanalysis for each variant and quickly exceeds human capability. In this work, we demonstrate a document
analysis application with the orientation correction and document identification task carried out by supervised
machine learning techniques for a large, international airline. The documents have been amassed over forty
years with numerous variants and are mostly black and white, typically consist of text and lines, and some
have extensive noise. Low level symb
ols are extracted from the raw images and separated into partitions. The
partitions are used to generate statistical features which are then used to train the classifiers. We compare the
classifiers for each task (e.g. decision tree, support vector machine, and random forest) to choose the most
appropriate. We also perform feature selection to reduce the complexity of the document type classifiers.
These parsimonious models result in comparable accuracy with 80% or fewer features.
(More)