We revisit graph grammar and graph parsing as tools for recognizing graphics. A top-down approach for parsing families of handwritten graphics containing different kinds of symbols and of structural relations is proposed. It has been tested on two distinct domains, namely the recognition of handwritten mathematical expressions and of handwritten flowcharts. In the proposed approach, a graphic is considered as a labeled graph generated by a graph grammar. The recognition problem is translated into a graph parsing problem: Given a set of strokes (input data), a parse tree which represents the best interpretation is extracted. The graph parsing algorithm generates multiple interpretations (consistent with the grammar) that can be ranked according to a global cost function that takes into account the likelihood of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to rules defined in the graph grammar. To constrain the number of partitions to be evaluated, we propose the use of a hypothesis graph, built from data-driven machine learning techniques, to encode the most likely symbol and relation hypotheses. Within this approach, it is easy to relax the stroke ordering constraint allowing interspersed symbols, as opposed to some previous works. Experiments show that our method obtains accuracy comparable to methods specifically developed to recognize domain-dependent data.

中文翻译：

---

在线手写图形识别的通用框架

我们重新讨论图文法和图解析作为识别图形的工具。提出了一种自顶向下的方法，用于解析包含各种符号和结构关系的手写图形族。它已经在两个不同的领域进行了测试，即手写数学表达式和手写流程图的识别。在提出的方法中，图形被视为由图文法生成的标记图。识别问题转化为图形分析问题：给定一组笔画（输入数据），提取代表最佳解释的分析树。图解析算法生成多种解释（与语法一致），这些解释可以根据考虑了符号和结构的可能性的全局成本函数进行排序。解析算法包括根据图文法中定义的规则对笔划集进行递归划分。为了限制要评估的分区的数量，我们建议使用从数据驱动的机器学习技术构建的假设图来编码最可能的符号和关系假设。在这种方法中，与某些先前的工作相反，轻松放宽允许散布符号的笔划顺序约束。实验表明，我们的方法获得的准确度可与专门为识别域相关数据而开发的方法相媲美。编码最可能的符号和关系假设。在这种方法中，与某些先前的工作相反，轻松放宽允许散布符号的笔划顺序约束。实验表明，我们的方法获得的准确度可与专门为识别域相关数据而开发的方法相媲美。编码最可能的符号和关系假设。在这种方法中，与某些先前的工作相反，轻松放宽允许散布符号的笔划顺序约束。实验表明，我们的方法获得的准确度可与专门为识别域相关数据而开发的方法相媲美。