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TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text

Lookup NU author(s): Dr Chris Willcocks, Dr Stephen McGough, Professor Boguslaw Obara

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2018.

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Abstract

Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be presented with a large volume of scanned documents which need to be processed in a finite time. However, this problem is exacerbated both by the volume, in terms of scanned documents and the complexity of the pages, which need to be processed. Often containing many different elements, which each need to be processed and understood. Text recognition, which is a primary task of this process, is usually depen- dent upon the type of text, being either handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be applied. This poses a more challenging task if a document contains both handwritten and machine-printed text. In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance. We realize the proposed process flow using several open-source image processing and text recog- nition packages. The evaluation is performed using a specially developed variant, presented in this work, of the IAM handwriting database, where we achieve an average transcription accuracy of nearly 80% for pages containing both printed and handwritten text.


Publication metadata

Author(s): Medhat F, Mohammadi M, Jaf S, Willcocks CG, Breckon TP, Matthews P, McGough AS, Theodoropoulos G, Obara B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Conference on Big Data (Big Data 2018)

Year of Conference: 2018

Online publication date: 24/01/2019

Acceptance date: 10/11/2018

Date deposited: 21/11/2018

Publisher: IEEE

URL: https://doi.org/10.1109/BigData.2018.8622136

DOI: 10.1109/BigData.2018.8622136

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538650356


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