Manufacturing companies rely on technical drawings to develop new designs or adapt designs to customer preferences. The database of historical and novel technical drawings thus represents the knowledge that is core to their operations. With current methods, however, utilizing these drawings is mostly a manual and time consuming effort. In this work, we present a software tool that knows how to interpret various parts of the drawing and can translate this information to allow for automatic reasoning and machine learning on top of such a large database of technical drawings. For example, to find erroneous designs, to learn about patterns present in successful designs, etc. To achieve this, we propose a method that automatically learns a parser capable of interpreting technical drawings, using only limited expert interaction. The proposed method makes use of both neural methods and symbolic methods. Neural methods to interpret visual images and recognize parts of two-dimensional drawings. Symbolic methods to deal with the relational structure and understand the data encapsulated in complex tables present in the technical drawing. Furthermore, the output can be used, for example, to build a similarity based search algorithm. We showcase one deployed tool that is used to help engineers find relevant, previous designs more easily as they can now query the database using a partial design instead of through limited and tedious keyword searches. A partial design can be a part of the two-dimensional drawing, part of a table, part of the contained textual information, or combinations thereof.