Handwritten and machine-printed text discrimination using a template matching approach
Data(s) |
2016
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Resumo |
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. |
Identificador |
http://eprints.aston.ac.uk/28913/1/Handwritten_and_machine_printed_text_discrimination.pdf Emambakhsh, Mehryar; He, Yulan and Nabney, Ian (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. |
Publicador |
IEEE |
Relação |
http://eprints.aston.ac.uk/28913/ |
Tipo |
Book Section NonPeerReviewed |
Formato |
application/pdf |