Automatic Recognition of Engineering Drawings and Maps

Ejiri, Masakazu; Kakumoto, Shigeru; Miyatake, Takafumi; Shimada, Shigeru; Iwamura, Kazuaki

doi:10.1201/9781003066330-3

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…The figure shows that the length of one column on the base plate of Tonga is marked as 42 cheok by Ha BaeckWon. This value in terms of the current known Yeongjocheok is 42 × 3.06 = 128.52 cm, which gives 8.568 cm when reduced by 1 15 . That is, 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.…”

Section: Jaseungcha Dohaementioning

confidence: 95%

“…This indicates that the actual size of each part in the Jaseungcha was reduced by the scale of 1 15 and 1 10 in the drawings. The currently known standard lengths of Yeongjocheok are 1 cheok = 30.6 cm, 1 chon = 3.06 cm, and 1 pun = 0.3 cm.…”

Section: Jaseungcha Dohaementioning

confidence: 96%

“…Ha BaeckWon made detailed records of the length of the curved surface and the radius. He also explained that he did not go through the fabrication process of Eonjo, and only recorded the figures by calculation, with the drawing being based on a scale of 1 10 .…”

Section: Jaseungcha Dohaementioning

confidence: 99%

“…In recent years, 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][2][3][4][5]. For this reason, classical drawing recognition is difficult due to the presence of many different dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Restoration of Dimensions for Ancient Drawing Recognition

Rim

Kim

et al. 2021

Electronics

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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.

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Section: Jaseungcha Dohaementioning

confidence: 95%

Section: Jaseungcha Dohaementioning

confidence: 96%

Section: Jaseungcha Dohaementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Restoration of Dimensions for Ancient Drawing Recognition

Rim

Kim

et al. 2021

Electronics

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show abstract

“…With the recent developments of artificial intelligence, deep learning-based image recognition is being applied in various industries, such as autonomous driving [1], medical diagnoses [2], facial recognition [3], and smart farms [4]. In addition, various studies using deep learning have been conducted in the engineering field, such as drawing digitization [5], manufacturability verification [6], and fault diagnosis [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

et al. 2021

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As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, the present study proposes a method for recognizing various types of lines and flow arrows in image-format P&ID. The proposed method consists of three steps. In the first step of preprocessing, the outer border and title box in the diagram are removed. In the second step of detection, continuous lines are detected, and then line signs and flow arrows indicating the flow direction are detected. In the third step of post-processing, using the results of line sign detection, continuous lines that require changing of the line type are determined, and the line types are adjusted accordingly. Then, the recognized lines are merged with flow arrows. For verification of the proposed method, a prototype system was used to conduct an experiment of line recognition. For the nine test P&IDs, the average precision and recall were 96.14% and 89.59%, respectively, showing high recognition performance.

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Trends in pattern recognition

Gesù

1993

Intelligent Perceptual Systems

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Automatic Recognition of Engineering Drawings and Maps

Cited by 6 publications

References 1 publication

Restoration of Dimensions for Ancient Drawing Recognition

Restoration of Dimensions for Ancient Drawing Recognition

Deep Learning-Based Method to Recognize Line Objects and Flow Arrows from Image-Format Piping and Instrumentation Diagrams for Digitization

Trends in pattern recognition

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