Abstract
This study aims to investigate and determine the actual size of the “cheok” scale—The traditional weights and measures of Korea—To aid in data construction on the recognition of ancient drawings in the field of artificial intelligence. The cheok scale can be divided into Yeongjocheok, Jucheok, Pobaekcheok, and Joryegicheok. This study calculated the actual dimensions used in the drawings of Tonga and Eonjo contained in Jaseungcha Dohae by Gyunam Ha BaeckWon, which helped us analyze the scale used in the southern region of Korea in the 1800s. The scales of 1/15 cheok and 1/10 cheok were used in the Tonga and Eonjo sections in Jaseungcha Dohae, and the actual dimensions in the drawing were converted to the scale used at the time. Owing to the conversion, the dimensions in the drawings of Tonga were converted to 30.658 cm per cheok, and ~31.84 cm per cheok for Eonjo. In this manner, the actual dimensions used in the southern region of Korea around the year 1800 were restored. Through this study, the reference values for drawing recognition of machinery drawings in Korea around 1800 were derived.
Highlights
Long before deep learning technology was commonly utilized for image recognition, there was continuous research on the recognition of engineering drawings
The length of the column on the base plate of the Tonga drawing in the original copy of Jaseungcha Dohae is measured at 8.568 cm, which indicates that Ha BaeckWon prepared the drawing at a scale of 1/15 according to the standards of Yeongjocheok
If Yeongjocheok is calculated based on the actual dimensions, the value is 310.41 mm, which after scale conversion is equivalent to approximately 31 cm
Summary
Long before deep learning technology was commonly utilized for image recognition, there was continuous research on the recognition of engineering drawings. Deep learning development tools such as TensorFlow, Keras, PyTorch, and Theano have been released These tools support efficient construction, training, and use of deep learning models and parallel processing using GPUs without extra coding, which makes it convenient for non-experts to utilize deep learning technology in their research [1–5]. For this reason, classical drawing recognition is difficult due to the presence of many different dimensions.
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