A brake disc character recognition method based on photometric stereo and surface curvature extraction

2019 ◽  
Author(s):  
Shuyi Liu ◽  
Yan Li ◽  
Aiguo Zhao ◽  
Keping Liu ◽  
Jianyu Wang
Keyword(s):  
Character Recognition ◽  
Photometric Stereo ◽  
Surface Curvature ◽  
Brake Disc ◽  
Recognition Method

Application of Image Processing to the Vehicle License Plate Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1638-1641 ◽  
Author(s):  
Chun Yu Chen ◽  
Bao Zhi Cheng ◽  
Xin Chen ◽  
Fu Cheng Wang ◽  
Chen Zhang
Keyword(s):  
Traffic Engineering ◽  
Character Recognition ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Edge Extraction ◽  
Recognition Method ◽  
License Plate Location ◽  
Vehicle License Plate Recognition ◽  
Plate Location

At present, the traffic engineering and automation have developed, and the vehicle license plate recognition technology need get a corresponding improvement also. In case of identifying a car license picture, the principle of automatic license plate recognition is illustrated in this paper, and the processing is described in detail which includes the pre-processing, the edge extraction, the license plate location, the character segmentation, the character recognition. The program implementing recognition is edited by Matlab. The example result shows that the recognition method is feasible, and it can be put into practice.

scholarly journals Device-Free Handwritten Character Recognition Method Using Acoustic Signal

2021 ◽  
Vol 33 (5) ◽  
pp. 1082-1095
Author(s):  
Atsushi Ogura ◽  
◽  
Hiroki Watanabe ◽  
Masanori Sugimoto
Keyword(s):  
Ultrasonic Wave ◽  
Character Recognition ◽  
Low Noise ◽  
Recognition Method ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Average Accuracy ◽  
Audible Sound ◽  
Device Free ◽  
Additional Device

In this paper, we propose a method for recognizing handwritten characters by a finger using acoustic signals. This method is carried out using a smartphone placed on a flat surface, such as a desk. Specifically, this method uses an ultrasonic wave transmitted from the smartphone, which is reflected by the finger, and an audible sound is generated when writing a handwritten character. The proposed method does not require an additional device for handwritten character recognition because it uses the microphone/speaker built into the device. Evaluation results showed that it was able to recognize 36 types of characters with an average accuracy of 77.8% in a low noise environment for 10 subjects. In addition, it was verified that combining an audible sound and an ultrasonic wave in this method achieved higher recognition accuracy than when only an audible sound or an ultrasonic wave was used.

HANDPRINTED CHINESE CHARACTER RECOGNITION USING THE PROBABILITY DISTRIBUTION FEATURE

1994 ◽  
Vol 08 (05) ◽  
pp. 1241-1258 ◽  
Author(s):  
TZE FEN LI ◽  
SHIAW-SHIAN YU
Keyword(s):  
Probability Distribution ◽  
Character Recognition ◽  
Chinese Character ◽  
Recognition Rate ◽  
Bayes Rule ◽  
Chinese Characters ◽  
Two Dimensional ◽  
Shape Variation ◽  
Recognition Method ◽  
Chinese Character Recognition

A simplified Bayes rule is used to classify 5401 categories of handwritten Chinese characters. The main feature for the Bayes rule deals with the probability distribution of black pixels of a thinned character. Our idea is that each Chinese character indicated by the black pixels represents a probability distribution in a two-dimensional plane. Therefore, an unknown pattern is classified into one of 5401 different distributions by the Bayes rule. Since the handwritten character has an irregular shape variation, the whole character is normalized and then thinned. Finally, a transformation is used to spread the black pixels uniformly over the whole square plane, but it still keeps the relative positions of the original black pixels. The main feature gives an 88.65% recognition rate. In order to raise the recognition rate, 4 more subsidiary features are elaborately selected such that they are not affected much by the irregularly shaped variation. The 4 features raise the recognition rate to 93.43%. A 99.30% recognition rate is achieved if the top 10 categories of HCC are selected by our recognition method and 99.61% if the top 20 are selected.

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