In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision tasks 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.
In this work, we use PHOCnet REF as our model of reference.
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)
2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR)
Computer Science