Application of Machine Vision in Defects Inspection and Character Recognition of Nameplate Surface

2014 ◽  
Author(s):  
Jingmei Li ◽  
Weiguo Zhang ◽  
Ruili Han
Keyword(s):  
Machine Vision ◽  
Character Recognition ◽  
Defects Inspection

A kNN-based approach for the machine vision of character recognition of license plate numbers

2017 ◽  
Author(s):  
Ana Riza F. Quiros ◽  
Rhen Anjerome Bedruz ◽  
Aaron Christian Uy ◽  
Alexander Abad ◽  
Argel Bandala ◽  
...  
Keyword(s):  
Machine Vision ◽  
Character Recognition ◽  
License Plate

scholarly journals DEVELOPMENT OF A MACHINE-VISION-BASED SYSTEM FOR RECORDING OF FORCE CALIBRATION DATA

2013 ◽  
Vol 24 ◽  
pp. 1360018
Author(s):  
SUMET HEAMAWATANACHAI ◽  
KITTIPONG CHAEMTHET ◽  
TAWAT CHANGPAN
Keyword(s):  
Machine Vision ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Error ◽  
Numerical Data ◽  
Calibration Data ◽  
Trace Back ◽  
Measurement Results ◽  
Force Calibration ◽  
New System

This paper presents the development of a new system for recording of force calibration data using machine vision technology. Real time camera and computer system were used to capture images of the reading from the instruments during calibration. Then, the measurement images were transformed and translated to numerical data using optical character recognition (OCR) technique. These numerical data along with raw images were automatically saved to memories as the calibration database files. With this new system, the human error of recording would be eliminated. The verification experiments were done by using this system for recording the measurement results from an amplifier (DMP 40) with load cell (HBM-Z30-10kN). The NIMT's 100-kN deadweight force standard machine (DWM-100kN) was used to generate test forces. The experiments setup were done in 3 categories; 1) dynamics condition (record during load changing), 2) statics condition (record during fix load), and 3) full calibration experiments in accordance with ISO 376:2011. The captured images from dynamics condition experiment gave >94% without overlapping of number. The results from statics condition experiment were >98% images without overlapping. All measurement images without overlapping were translated to number by the developed program with 100% accuracy. The full calibration experiments also gave 100% accurate results. Moreover, in case of incorrect translation of any result, it is also possible to trace back to the raw calibration image to check and correct it. Therefore, this machine-vision-based system and program should be appropriate for recording of force calibration data.

An online defects inspection method for float glass fabrication based on machine vision

2007 ◽  
Vol 39 (11-12) ◽  
pp. 1180-1189 ◽  
Author(s):  
Xiangqian Peng ◽  
Youping Chen ◽  
Wenyong Yu ◽  
Zude Zhou ◽  
Guodong Sun
Keyword(s):  
Machine Vision ◽  
Float Glass ◽  
Inspection Method ◽  
Defects Inspection

scholarly journals Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Linjian Lei ◽  
Shengli Sun ◽  
Yue Zhang ◽  
Huikai Liu ◽  
Hui Xie
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Character Recognition ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Accuracy ◽  
Bearing Surface ◽  
Surface Defect Detection

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

scholarly journals An Engraving Character Recognition System Based on Machine Vision

2017 ◽  
Author(s):  
WANG YU ◽  
ZHIHENG WU ◽  
HONGBIN LIU ◽  
QIYU CHEN ◽  
XIANYUN DUAN ◽  
...  
Keyword(s):  
Machine Vision ◽  
Character Recognition ◽  
Recognition System
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