Learning Models of Handwriting for Structured Texture Synthesis

这项研究的目的是使用一种新的生成数学模型来生成不同人的笔迹风格和要“伪造”的指定文本串的笔迹图像。“我们的模型使用真实的笔迹样本的图像片段，并学习哪些片段在什么变形下是兼容的。我们预期的分析/合成应用包括法律的案件中笔迹的定量认证，历史文件损坏部分的修复，银行目的的笔迹模仿，以及最终，特定于作者的笔迹识别和绘制草图解释。该项目的具体目标是：- 自动合成小说文本的笔迹，但以特定的人的风格。测量给定样本是由特定个人编写的可能性。建立一个系统，建议下一步应该捕获哪些笔迹示例。项目范围仅限于笔迹，以评估可行性。在它的结论，我们希望进一步追求以下额外的目标：-展示大规模伪造研究的综合和分析方法。我们的目标是衡量我们的伪造和伪造检测率，以帮助将这些技术转化为工业实践。可以通过在写作时拍摄个人来限制伪造技术的滥用，以确认合成不是自动的。构建一个类似的自适应创作系统来帮助合成卡通动画。用基于简笔画的肢体配置（2D关节角度）的结构替换当前项目中使用的字符串。探索基于示例的生成模型的统一框架。在许多领域，当有多个例子时，更好的综合应该是可能的。这项关于手写的近期研究的结果应该具有普遍性，因此训练示例的结构化获取可以稳步提高合成绘图，3D形状和视频后期制作的质量。

The proposed research aims to produce images of handwriting using a new generative mathematical model of different people's handwriting styles and the specified text strings to be "forged." Our model uses image-fragments of real handwriting samples and learns what fragments are compatible under what deformations.Our intended analysis/synthesis applications include quantitative authentication of handwriting in legal cases, inpainting of destroyed sections of historical documents, imitation of handwriting for banking purposes, and eventually, writer-specific handwriting recognition and drawn-sketch interpretation.The specific objectives of this project are to:- Automatically synthesize handwriting of novel text but in a particular person's style.- Measure the likelihood that a given sample was written by a specific individual.- Build a system that suggests what examples of handwriting should be captured next.The project scope is limited to just handwriting to assess feasibility. At its conclusion, we expect to further pursue the following additional objectives:- Demonstrate the synthesis and analysis approaches on large-scale forgery studies. The goal is to measure our forgery and forgery-detection rates to help transfer these techniques into industrial practice. Abuse of the technology for forgery creation could be limited by filming an individual while writing, to confirm that the synthesis was not automatic.- Build a similar adaptive authoring system to help synthesize cartoon animation. Replace strings of characters used in the present project with a structure based on limb-configurations (2D joint-angles) of a stick-figure.- Explore a unified framework for example-based generative models. In many domains, better synthesis should be possible when multiple examples are available. Findings from this near-term research on handwriting should generalize, so that structured acquisition of training examples steadily improves the quality of synthesized drawings, 3D shapes, and video post-production.