Humans have the privilege to automatically have a deep understanding of technical documents, since they have the ability to deal with complex concepts coming from many different modalities, like diagrams, text, tables, formulas, graphics, pictures, etc. For many years researchers are working to transfer such potential to AI based machines. This paper takes the advantage of the synergistic and interactive enrichment of two TD modalities, the block diagrams and the associated natural language text, obtained to automatically generate pseudocode that describes the functionality of the system under study. The methodology for generating the code is mainly based on the mapping of the TD modalities into Stochastic Petri-nets (SPN) that enriches the system diagrams, from which the pseudocode is generated. The overall methodology will contribute to an automatic deep understanding of technical documents (TD) without the main involvement of humans. Two illustrative examples are also provided for describing the methodology.

中文翻译：

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深入理解技术文档：第二部分。自动提取伪代码

人类有特权自动深入理解技术文档，因为他们有能力处理来自许多不同模式的复杂概念，如图表、文本、表格、公式、图形、图片等。多年来，研究人员努力将这种潜力转移到基于人工智能的机器上。本文利用两种 TD 模态、框图和相关的自然语言文本的协同和交互丰富的优势，自动生成描述所研究系统功能的伪代码。生成代码的方法主要基于 TD 模态到随机 Petri 网 (SPN) 的映射，后者丰富了系统图，从中生成伪代码。整个方法将有助于在没有人类主要参与的情况下自动深入理解技术文档 (TD)。还提供了两个说明性示例来描述该方法。