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

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Abstract

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.

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  • Handwritten and machine-printed text discrimination

    Rights statement: © 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.

    Accepted author manuscript, 816 KB, PDF-document

Details

Publication date2016
Publication titleProceedings : 12th IAPR International Workshop on Document Analysis Systems, DAS 2016
PublisherIEEE
Pages399-404
Number of pages6
ISBN (Print)978-1-5090-1792-8
Original languageEnglish
Event12th IAPR International Workshop on Document Analysis Systems - Santorini, Greece

Workshop

Workshop12th IAPR International Workshop on Document Analysis Systems
Abbreviated titleDAS 2016
CountryGreece
CitySantorini
Period11/04/1614/04/16

Bibliographic note

-© 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.

Keywords

  • classification, handwritten, machine-printed, OCR, shape analysis, template matching

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