This paper is designed to have an opticalcharacterrecognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity s...
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ISBN:
(纸本)9781665483797
This paper is designed to have an opticalcharacterrecognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity size and part number found on the labels is time consuming, more prone to errors and utilizes more manpower. Automating the inspection through opticalcharacterrecognition using image pre-processing and machine vision contributes to an easier inspection process, better management of records and faster cycle time. The images captured using a vision camera went through different stages of image pre-processing via OpenCV-Python and recognition through Google tesseract. Different categorical variables including exposure time and location of texts in a captured image were used to determine and improve the overall recognition accuracy. By improving the lighting condition through the addition of light sources, the developed OCR system was able to achieve a characterrecognition accuracy of 99.375%.
Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in *** video obtained from such surveillance are of low *** counting vehicles from such videos are necessity to avoid tra...
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Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in *** video obtained from such surveillance are of low *** counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more *** the other hand,detecting vehicles from such low quality videos are highly challenging with vision based *** this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available *** this work profound Detection Transformer(DETR)model is used for object(vehicle)*** vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real *** frame in the traffic footage has its date and time which is detected and retrieved using tesseractopticalcharacter *** date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR *** furnishes the vehicles report with *** Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been *** proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.
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