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Text Detection and Recognition on Traffic Panels From Street Level Imagery Using Visual Appearance
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Text Detection and Recognition on Traffic Panels From Street - Level Imagery Using Visual Appearance

Category : Automation


Sub Category : Machine vision


Project Code : ITAM19


Project Abstract

    Detection and recognition of traffic signs has long been in the center of interest for having great impact on the safety of the driver. However, it remains some problems. First, it is difficult to track an object in an image sequence captured from a vehicle undergoing a generally non uniform motion. Second, air pollution and weather conditions may decrease the visibility of road signs. Besides that, outdoor lighting conditions vary from day to night and may affect the apparent colors of road signs. Finally, recognition system has to be fast and robust which increase the difficulty of the automatic detection of the road signs. For all these reasons, reliable detection system of road signs from various scenes becomes rather challenging. The detection module segments the input image in the YCBCR color space, and then detects road signs using a shape filtering method. The classification module determines the type of detected road signs using a Multi-layer Perception neural network.

EXISTING SYSTEM

PROPOSED SYSTEM

 

Ø  In the existing system, the road signs are identified manually by the driver on their own.

Ø  The disadvantage is that the driver may miss out some signs by mistake.

           

Drawbacks of existing System:

Ø  Manual error may occur

Ø  Causes accident

Ø  Violation of traffic rules

 

 

Ø  We propose a new automated system to recognize the road sign using camera

Ø  Image processing   technique is used to recognize the road sign

 

                        

Advantage of proposed system:

Ø  Automatic control

Ø  Fast response & Reduced error

Ø  Efficient system

 


 
 
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