Use of Cognitive Reading Models in Handwriting Recognition

书面语言识别不仅是一种方便、自然的人机交互方式，而且是人工智能领域的重要挑战，具有重要意义。它作为计算机输入界面的重要性，在自然度和使用的方便性方面可与语音相媲美。尽管来自认知阅读理论的有力证据表明，文本识别是一个互动的过程，在每个阶段都能有效地利用资源，但传统的手写识别系统采用了单调级联的架构。本研究的主要目标是开发一种基于认知阅读理论的手写识别计算模型。该模型将考虑到迄今为止文献中尚未解决的手写识别的几个方面。单词识别模型的结构将允许它主动地从早期的处理阶段寻找信息。根据图像的质量和词典的难度，特征提取器将只发送可能特征的一个子集的信息，允许内部决策者评估这些信息并在需要时请求更多信息。

Written Language Recognition has great significance not only as a convenient and natural way of interacting with computers but also as an important challenge in AI. Its importance as an input interface to computers is comparable to that of speech in terms of naturalness and convenience of use.Notwithstanding the strong evidence from cognitive reading theories that suggests that text recognition is an interactive process that makes efficient use of resources at every stage, traditional handwriting recognition systems have employed a monotonically cascaded architecture. The primary goal of this proposed research is to develop a computational model of handwriting recognition that is grounded in cognitive reading theories. The model will account for several aspects of handwriting recognition that have not been addressed in the literature to date. The architecture of the word recognition model will allow it to actively seek information from earlier processing stages. Depending on the quality of the image and the difficulty of the lexicon, the feature extractor will send information on only a subset of possible features, allowing an internal decision-maker to evaluate the information and request more if needed.