Handwritten and machine-printed text discrimination using a template matching approach


We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.

Publication DOI: https://doi.org/10.1109/DAS.2016.22
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
?? 50811700Jl ??
Additional Information: -© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 12th IAPR International Workshop on Document Analysis Systems
Event Type: Other
Event Dates: 2016-04-11 - 2016-04-14
Uncontrolled Keywords: classification,handwritten,machine-printed,OCR,shape analysis,template matching,Computer Networks and Communications,Computer Vision and Pattern Recognition,Library and Information Sciences
ISBN: 978-1-5090-1792-8
Last Modified: 01 Jul 2024 07:37
Date Deposited: 18 Aug 2016 13:30
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... rnumber=7490151 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2016-06-13
Accepted Date: 2016-01-01
Authors: Emambakhsh, Mehryar
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Nabney, Ian (ORCID Profile 0000-0003-1513-993X)



Version: Accepted Version

Export / Share Citation


Additional statistics for this record