PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents. Sudholt, S. & Fink, G. A. January, 2017. 🏷️ /unread、Computer Science - Computer Vision and Pattern Recognition![PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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. 【摘要翻译】近年来，深度卷积神经网络在分类、检测或分割等各种计算机视觉任务中都取得了一流的性能。由于其出色的性能，CNN 在文档图像分析领域的应用也越来越广泛。在这项工作中，我们提出了一种使用最近提出的 PHOC 表示法进行训练的 CNN 架构。我们通过实验证明，我们的 CNN 架构在各种单词识别基准测试中的表现都优于目前的技术水平，同时训练和测试时间都很短。
@misc{sudholt2017,
title = {{PHOCNet}: {A} {Deep} {Convolutional} {Neural} {Network} for {Word} {Spotting} in {Handwritten} {Documents}},
shorttitle = {{PHOCNet}：用于手写文档中单词识别的深度卷积神经网络},
url = {http://arxiv.org/abs/1604.00187},
doi = {10.48550/arXiv.1604.00187},
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.
【摘要翻译】近年来，深度卷积神经网络在分类、检测或分割等各种计算机视觉任务中都取得了一流的性能。由于其出色的性能，CNN 在文档图像分析领域的应用也越来越广泛。在这项工作中，我们提出了一种使用最近提出的 PHOC 表示法进行训练的 CNN 架构。我们通过实验证明，我们的 CNN 架构在各种单词识别基准测试中的表现都优于目前的技术水平，同时训练和测试时间都很短。},
language = {en},
urldate = {2023-11-17},
publisher = {arXiv},
author = {Sudholt, Sebastian and Fink, Gernot A.},
month = jan,
year = {2017},
note = {🏷️ /unread、Computer Science - Computer Vision and Pattern Recognition},
keywords = {/unread, Computer Science - Computer Vision and Pattern Recognition},
}
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