Attribute CNNs for Word Spotting in Handwritten Documents

Sebastian Sudholt; Gernot Fink

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arXiv · 1712.07487

Attribute CNNs for Word Spotting in Handwritten Documents

Sebastian Sudholt·Gernot Fink

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Abstract

Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute representation. At their time, this influential method defined the state-of-the-art in segmentation-based word spotting. In this work, we present an approach for learning attribute representations with Convolutional Neural Networks (CNNs). By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings. In addition, we propose two different CNN architectures, specifically designed for word spotting. These architectures are able to be trained in an end-to-end fashion. In a number of experiments, we investigate the influence of different word string embeddings and optimization strategies. We show our Attribute CNNs to achieve state-of-the-art results for segmentation-based word spotting on a large variety of data sets.

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Sebastian Sudholt, Gernot Fink. 2017-12-20. Attribute CNNs for Word Spotting in Handwritten Documents. https://arxiv.org/abs/1712.07487

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