RetinaNet multiscale embeddings improve pattern location accuracy and reduce storage needs versus prior methods on the DocExplore dataset, though they fail on pages with multiple query instances.
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
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abstract
In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state of the art results for various word spotting benchmarks while exhibiting short training and test times.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Pattern Spotting in Historical Documents Using Convolutional Models
RetinaNet multiscale embeddings improve pattern location accuracy and reduce storage needs versus prior methods on the DocExplore dataset, though they fail on pages with multiple query instances.