Handwritten and Machine-Printed Text Discrimination Using a Template Matching Approach

Mehryar Emambakhsh, Yulan He, Ian Nabney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2016 12th IAPR Workshop on Document Analysis Systems (DAS)
Number of pages6
ISBN (Electronic)9781509017928
Publication statusPublished - 13 Jun 2016


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