scholarly journals A Real-Time Line Segmentation Algorithm for An Offline Overlapped Handwritten Jawi Character Recognition Chip

2007 ◽  
Vol 20 (2) ◽  
pp. 171-182 ◽  
Author(s):  
Zaidi Razak ◽  
Khansa Zulkiflee ◽  
Rosli Salleh ◽  
Mashkuri Yaacob ◽  
Emran Mohd Tamil
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Segmentation Algorithm ◽  
Time Line ◽  
Line Segmentation

Real-Time Line Detection Method for Camera Images of the Rescue Robot

2010 ◽  
Vol 130 (11) ◽  
pp. 2039-2046
Author(s):  
Munetoshi Numada ◽  
Masaru Shimizu ◽  
Takuma Funahashi ◽  
Hiroyasu Koshimizu
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Line Detection ◽  
Time Line ◽  
Rescue Robot

Real-time line segments detection based on graphic processor

2009 ◽  
Vol 29 (5) ◽  
pp. 1359-1361
Author(s):  
Tong ZHANG ◽  
Zhao LIU ◽  
Ning OUYANG
Keyword(s):  
Real Time ◽  
Time Line ◽  
Line Segments ◽  
Graphic Processor

Real-time line-structured light measurement system based on Scheimpflug principle

2021 ◽  
Author(s):  
Yong Zhang ◽  
Yucheng Qian ◽  
Shanlin Liu ◽  
Yunfei Wang
Keyword(s):  
Real Time ◽  
Measurement System ◽  
Structured Light ◽  
Time Line ◽  
Light Measurement

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

A Novel VLSI Architecture for Real-Time Line-Based Wavelet Transform Using Lifting Scheme

2007 ◽  
Vol 22 (5) ◽  
pp. 661-672 ◽  
Author(s):  
Kai Liu ◽  
Ke-Yan Wang ◽  
Yun-Song Li ◽  
Cheng-Ke Wu
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Vlsi Architecture ◽  
Lifting Scheme ◽  
Time Line

scholarly journals Unconstrained Handwritten Text Line Segmentation for Kannada Language

2019 ◽  
Vol 8 (12) ◽  
pp. 953-956
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Text Line ◽  
Connected Component ◽  
Horizontal Projection ◽  
Text Documents ◽  
Handwritten Text ◽  
Kannada Language ◽  
System Separation ◽  
Line Segmentation

Segmentation is division of something into smaller parts and one of the Component of character recognition system. Separation of characters, words and lines are done in Segmentation from text documents. character recognition is a process which allows computers to recognize written or printed characters such as numbers or letters and to change them into a form that the computer can use. the accuracy of OCR system is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. The main aim of this paper is to find out the various text line segmentations are Projection profiles, Weighted Bucket Method. Proposed method is horizontal projection profile and connected component method on Handwritten Kannada language. These methods are used for experimentation and finally comparing their accuracy and results.

Sign in / Sign up

Export Citation Format

Share Document