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Evaluating synthetic pre-Training for handwriting processing tasks

Abstract: In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision sig-nal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be consid-ered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strat-egy allows extracting rich representations of the writers' style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches.& COPY; 2023 Elsevier B.V. All rights reserved.


Pippi, V.; Cascianelli, S.; Baraldi, L.; Cucchiara, R. "Evaluating synthetic pre-Training for handwriting processing tasks" PATTERN RECOGNITION LETTERS, vol. 172, pp. 44 -50 , 2023 DOI: 10.1016/j.patrec.2023.06.003

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