License Plate Recognition

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Instructions
Input - Click on the “+” button in the video player and upload a decent resolution video with less than 40mb.
Output - Inferred video with detected registration numbers of the vehicles at specified time in JSON format.
Note: Upload the appropriate video for optimal performance. Video that doesn't relate to the model might provide you unexpected results.

The process uses multiple deep neural networks to detect and identify vehicle license plate, Given the generality of traffic cameras and toll plaza cameras, license plate recognition (LPR) may seem pretty straightforward, just by taking the video source, analysing and then processing it with multiple deep neural networks to fetch the license plate from numerous vehicles while maintaining the accuracy levels is a challenging task. At TensorGo, we solve problems with our SOTA techniques.

The extraction of the license plate can be deployed on a large scale, where it plays a very vital role in mass surveillance to track and detect repeated offenders.This system can be automated by ingesting the video feed from traffic cameras, public security cameras, toll plaza cameras and analyse all the license plates in matter of seconds.This will be a very efficient for the law enforcement authorities while bringing governance in to the system. With the rapid development and expansion of the cities and its road network, only automation can track and enable law enforcement to track traffic offenders using license plate recognition (LPR).

Use Cases

Detecting Vehicle Types, License Plate Detection, Parking Zones, Law Enforcements etc.,

API Request and Response

TensorGo Platform provides you with a one stop solution to customize the our API offerings as per your use case by mixing and matching the existing APIs or requesting for a new custom model. This accelerates the development of use cases with minimal or no code towards deep learning applications.
The endpoint for API is:
[URL]
Request:
post/
[URL]
	body:{
		id:”Some Unique  ID”
		app:”Name of the app”
		file:”Attached File”
              }
Example:
post/
	[URL]
	body:{
		id:xsk231ds168wd
		app:lpr
		file:”Attached File”
             }

Response:
	{ msg:Uploaded }

Download the Uploaded video:
get/
	[URL]
	body:{
		id:xsk231ds168wd
		app:lpr
              }