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Publication - Professor Ian Nabney

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

    Citation

    Emambakhsh, M, He, Y & Nabney, I, 2016, ‘Handwritten and machine-printed text discrimination using a template matching approach’. in: Proceedings : 12th IAPR International Workshop on Document Analysis Systems, DAS 2016. IEEE Computer Society, United States, pp. 399-404

    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.

    Full details in the University publications repository