Owing to the rapid growth of touchscreen mobile terminals and pen-based
interfaces, handwriting-based writer identification systems are attracting
increasing attention for personal authentication, digital forensics, and other
applications. However, most studies on writer identification have not been
satisfying because of the insufficiency of data and difficulty of designing
good features under various conditions of handwritings. Hence, we introduce an
end-to-end system, namely DeepWriterID, employed a deep convolutional neural
network (CNN) to address these problems. A key feature of DeepWriterID is a new
method we are proposing, called DropSegment. It designs to achieve data
augmentation and improve the generalized applicability of CNN. For sufficient
feature representation, we further introduce path signature feature maps to
improve performance. Experiments were conducted on the NLPR handwriting
database. Even though we only use pen-position information in the pen-down
state of the given handwriting samples, we achieved new state-of-the-art
identification rates of 95.72% for Chinese text and 98.51% for English text.

该研究引入了一个名为 DeepWriterID 的系统，该系统利用深度卷积神经网络进行笔迹、手写体识别，并引入了一种名为 DropSegment 的新方法来实现数据增强和改善 CNN 的泛化适用性。通过在笔放置状态下只使用笔位信息，该系统在 NLPR 手写数据库中实现了 95.72％的中文文本识别率和 98.51％的英文文本识别率，创造了新的最高记录。

DeepWriterID：一种端到端的在线无文本依赖的笔迹识别系统

DeepWriterID: An End-to-end Online Text-independent Writer  Identification System

Structured prediction tasks pose a fundamental trade-off between the need for
model complexity to increase predictive power and the limited computational
resources for inference in the exponentially-sized output spaces such models
require. We formulate and develop the Structured Prediction Cascade
architecture: a sequence of increasingly complex models that progressively
filter the space of possible outputs. The key principle of our approach is that
each model in the cascade is optimized to accurately filter and refine the
structured output state space of the next model, speeding up both learning and
inference in the next layer of the cascade. We learn cascades by optimizing a
novel convex loss function that controls the trade-off between the filtering
efficiency and the accuracy of the cascade, and provide generalization bounds
for both accuracy and efficiency. We also extend our approach to intractable
models using tree-decomposition ensembles, and provide algorithms and theory
for this setting. We evaluate our approach on several large-scale problems,
achieving state-of-the-art performance in handwriting recognition and human
pose recognition. We find that structured prediction cascades allow tremendous
speedups and the use of previously intractable features and models in both
settings.

本文提出了一种基于‘Structured Prediction Cascade’的新型结构预测方法，通过一系列逐步添加模型的方式，可以有效实现对输出状态空间的过滤和精细调整，并且在优化过程中控制过滤效率与准确率的平衡，通过对模型进行不断优化，显著提升结构预测任务的效率与准确性。