Open AccessText area detection in handwritten documents scanned for further processing
Author(s) -
Jakub Pach,
Izabella Antoniuk,
Artur Krupa
Publication year - 2020
Publication title -
machine graphics and vision
Language(s) - English
Resource type - Journals
eISSN - 2720-250X
pISSN - 1230-0535
DOI - 10.22630/mgv.2020.29.1.2
Subject(s) - computer science , image processing , artificial intelligence , column (typography) , document processing , noise (video) , text recognition , binary image , pattern recognition (psychology) , line (geometry) , image (mathematics) , binary number , optical character recognition , computer vision , arithmetic , mathematics , telecommunications , geometry , frame (networking)
In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.