This paper introduces a very challenging dataset of historic German documents
and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate
handwritten annotations of any kind in these documents. The handwritten
annotations can appear in form of underlines and text by using various writing
instruments, e.g., the use of pencils makes the data more challenging. We train
and evaluate various end-to-end semantic segmentation approaches and report the
results. The task is to classify the pixels of documents into two classes:
background and handwritten annotation. The best model achieves a mean
Intersection over Union (IoU) score of 95.6% on the test documents of the
presented dataset. We also present a comparison of different strategies used
for data augmentation and training on our presented dataset. For evaluation, we
use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout
Analysis for Challenging Medieval Manuscripts.

本文介绍一个历史德语文件的数据集，并使用全卷积神经网络（FCNN）方法来定位这些文件中的手写注释，研究训练和评估了各种各样的端到端语义分割方法，并报告了结果，最佳模型可以在测试文档中获得 95.6％的平均交集联合得分（IoU）。

使用完全卷积网络识别具有挑战性的手写注释

Recognizing Challenging Handwritten Annotations with Fully Convolutional  Networks

In this paper, we propose a fast fully convolutional neural network (FCNN)
for crowd segmentation. By replacing the fully connected layers in CNN with 1
by 1 convolution kernels, FCNN takes whole images as inputs and directly
outputs segmentation maps by one pass of forward propagation. It has the
property of translation invariance like patch-by-patch scanning but with much
lower computation cost. Once FCNN is learned, it can process input images of
any sizes without warping them to a standard size. These attractive properties
make it extendable to other general image segmentation problems. Based on FCNN,
a multi-stage deep learning is proposed to integrate appearance and motion cues
for crowd segmentation. Both appearance filters and motion filers are
pretrained stage-by-stage and then jointly optimized. Different combination
methods are investigated. The effectiveness of our approach and component-wise
analysis are evaluated on two crowd segmentation datasets created by us, which
include image frames from 235 and 11 scenes, respectively. They are currently
the largest crowd segmentation datasets and will be released to the public.

本研究提出了一种快速卷积神经网络 (FCNN)，并使用多阶段的深度学习来整合外观和动态线索进行人群分割，成功使用我们自己创建的具有 235 和 11 个场景的人群分割数据集对我们的方法进行了评估。