An effective approach in computerizing the paper engineering drawings with some understanding capabilities

1988 ◽  
Author(s):  
A. K.-C. King ◽  
W.-J. Tseng ◽  
H.-D. Yeuh
Keyword(s):  
Engineering Drawings ◽  
Paper Engineering

Perancangan Buku Cerita Pop-Up Petualangan Lentera Kunang-Kunang

2016 ◽  
Vol 8 (1) ◽  
pp. 23-31
Author(s):  
Elizabeth Jessica Sentani
Keyword(s):  
Social Skills ◽  
Social Interaction ◽  
Direct Observation ◽  
Observation Data ◽  
Literature Study ◽  
Child Psychologists ◽  
Paper Engineering

This research enables the design of a pop-up storybook about cooperating while learning and about social skills. This pop-up storybook is made to remind the importance of cooperation for children age 4-6. The research is made because of the decline in cooperation among children which is the result of lack of social interaction, development in technology, and parenting issues. The research covers data collected about children age 4-6, interviews with child psychologists, literature study, dan direct observation. Data results were then processed to build story, visual, and paper engineering techniques. Keywords: storybook, pop-up, social skills, cooperation, children

Understanding engineering drawings

1982 ◽  
Vol 20 (3) ◽  
pp. 244-258 ◽  
Author(s):  
Robert M. Haralick ◽  
David Queeney
Keyword(s):  
Engineering Drawings

ConvNet-Based Optical Recognition for Engineering Drawings

2017 ◽  
Author(s):  
Andrew Brock ◽  
Theodore Lim ◽  
J. M. Ritchie ◽  
Nick Weston
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Cross Validation ◽  
Convolutional Networks ◽  
Optical Character ◽  
Engineering Drawings ◽  
Machine Analysis ◽  
Fold Cross Validation ◽  
Optical Recognition

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.

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