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.
|Title of host publication||Proceedings : 12th IAPR International Workshop on Document Analysis Systems, DAS 2016|
|Place of Publication||United States|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Publication status||Published - 13 Jun 2016|
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- classification, handwritten, machine-printed, OCR, shape analysis, template matching