A comparison of top-down and bottom-up approaches to recognizing component assemblies in image mining electronic circuits
Creators: name: J Turkel, William
name: Sirohey, Zain
Publisher: name: unknown
Year: 2020
Subjects: subject: Methodology
subject: Research
subject: Digital humanities
subject: Machine learning
subject: Electronic information resources
subject: Computer vision

description: Historians of electronics (and subjects that depend on electronics like communications, instrumentation, and computation) have access to a vast digitized archive of primary sources. The majority of these sources are freely available. Turkel and various collaborators have used web crawlers to collect millions of pages of these documents to subject to automated analysis with text and image mining. Electronic schematics and circuit diagrams, which are ubiquitous in these sources, can be approached as a special case of line drawing. They provide interesting opportunities and challenges for the use of computer vision and image mining in historical research.
In this paper, we present work-in-progress comparing two approaches to the problem of contextualization, the creation of functional tools built on image mining. An electronic schematic shows the interconnection of various kinds of components. Automating the recognition of these components is one step in a workflow for handling schematics. Historians of electronics are typically interested in meaningful assemblies that occur above the level of individual components, however. This is analogous to text mining, which becomes more useful at levels of analysis above the individual character.
In the bottom-up approach, we start by recognizing the graphical symbols for individual components as assemblies of graphical primitives like lines and arcs, then build assemblies from those components. In the top-down approach, we start by analysing the whole schematic into components and connections, then abstract away from the components to extract the connections and the nodes where they meet.
description: Historians of electronics (and subjects that depend on electronics like communications, instrumentation, and computation) have access to a vast digitized archive of primary sources. The majority of these sources are freely available. Turkel and various collaborators have used web crawlers to collect millions of pages of these documents to subject to automated analysis with text and image mining. Electronic schematics and circuit diagrams, which are ubiquitous in these sources, can be approached as a special case of line drawing. They provide interesting opportunities and challenges for the use of computer vision and image mining in historical research. In this paper, we present work-in-progress comparing two approaches to the problem of contextualization, the creation of functional tools built on image mining. An electronic schematic shows the interconnection of various kinds of components. Automating the recognition of these components is one step in a workflow for handling schematics. Historians of electronics are typically interested in meaningful assemblies that occur above the level of individual components, however. This is analogous to text mining, which becomes more useful at levels of analysis above the individual character. In the bottom-up approach, we start by recognizing the graphical symbols for individual components as assemblies of graphical primitives like lines and arcs, then build assemblies from those components. In the top-down approach, we start by analysing the whole schematic into components and connections, then abstract away from the components to extract the connections and the nodes where they meet.