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A method of finding and classifying various components and objects in adesign diagram, drawing, or planning layout is proposed. The methodautomatically finds the objects present in a legend table and finds theirposition, count and related information with the help of multiple deep neuralnetworks. The method is pre-trained on several drawings or design templates tolearn the feature set that may help in representing the new templates. For atemplate not seen before, it does not require any training with templatedataset. The proposed method may be useful in multiple industry applicationssuch as design validation, object count, connectivity of components, etc. Themethod is generic and domain independent.

Automatic Detection and Classification of Symbols in Engineering Drawings