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Automatic Number Plate Recognition

Aayush David 2K10/EC/003Abhishek Choudhary 2K10/EC/009

Aman Bansal 2K10/EC/019Gaurav Keswani 2K10/EC/059

Minor Project

Contents

• Introduction• Need for ANPR• Algorithm• Working• Challenges• Future Work• Bibliography

IntroductionAutomatic License Plate Recognition (ALPR) is an image-processing technology used to identify vehicles license plates. It is an embedded system which has numerous applications and challenges. Few of the applications being unattended parking lots, security control of restricted areas, traffic law enforcement, and automatic toll collection.

Inspiration?

The entire inspiration behind implementing such a system to improve the efficiency and speed in these processes. Reduce manpower.

ALPR consists of two main steps :

• Locating License PlatesIn the first stage, license-plate candidates are determined based on the features of license plates. License plates come with a wide range of features in shape, symmetry, size, colour, texture etc.

• Recognizing CharactersThe second stage involves optical character recognition of the connected components as taken out from the images and writing them into text using OCR.

Need for ANPR

Travel Time & Source - Destination

Studies

Commercial Vehicle Safety

Revenue & Tax Collection

Tolling Applications

General Law Enforcement

Border Screening

DHS Screening and Interdiction

Transportation

Law Enforcement

Customs and

Immigration

Homeland Security

A Few Examples

Traffic Control & Transport PlanningBy using ANPR on this footage it is possible to monitor the travel of individual vehicles, automatically providing information about the speed and flow of various routes. These details can highlight problem areas as and when they occur and helps the centre to make informed incident management decisions.

Drive-through Customer Recognition

Hotel & Car Parking AutomationOne of the main applications of ANPR is parking automation and parking security: ticketless parking fee management, parking access automation, vehicle location guidance

Access ControlAccess control in general is a mechanisms for limiting access to areas and resources based on users' identities and their membership in various predefined groups

Algorithm

Capture Pre-Process Localize

Connected Component

Analysis

Segmentation

OCR

CAPTURE

The image of the vehicle is captured using a high resolution photographic camera. A better choice is an Infrared (IR)camera. The camera may be rolled and pitched with respect to the license plates.

For lack of resources, we used the 5MP camera of our smartphone for clicking test pictures.

PRE-PROCESS

Pre-processing is the set algorithms applied on the image to enhance the quality. For the present system pre-processing involves two processes: Resize – The image size from the camera might be large and can drive the system slow. It is to be resized to a feasible aspect ratio. Convert Colour Space – Images captured using IR or photographic cameras will be either in raw format or encoded into some multimedia standards. Normally, these images will be in RGB mode, with three channels (viz. red, green and blue) Number of channels defines the amount color information available on the image. The image has to be converted to grayscale.

LOCALIZE

There are two motivations for this operation 1. Highlighting characters and 2. Suppressing background.Localization is done by an image processing technique called Thresholding. The pixels of the image are truncated to two values depending upon the value of threshold. Threshold requires pre-image analysis for identifying the suitable threshold value. Adaptive thresholding technique determines a local optimal threshold value for each image pixel so as to avoid the problem originating from non uniform illumination.

CONNECTED COMPONENT ANALYSIS

In order to eliminate undesired image areas, a connected component algorithm is first applied to the binarized plate candidate. Connected component analysis is performed to identify the characters in the image. Basic idea is to traverse through the image and find the connected pixels. Each of the connected components (blobs) are labelled and extracted. Fig. 4 shows the filtered blobs

SEGMENTATION

Segmentation is the process of cropping out the labelled blobs. These blobs are expected to be the required portion of the license number. A special algorithm called Image Scissoring is introduced here. In this algorithm, the license plate is vertically scanned and scissored at the row on which there is no white pixel and the scissored area is copied into a new matrix, There are unwanted blobs even after segmentation. These are classified using special algorithms.

OCR (Optical Character Recognition)

In order to eliminate undesired image areas, a connected component algorithm is first applied to the binarized plate candidate. Connected component analysis is performed to identify the characters in the image. Basic idea is to traverse through the image and find the connected pixels. Each of the connected components (blobs) are labelled and extracted. Fig. 4 shows the filtered blobs

Process….

Test Image

Pre-process…Conversion, Noise Removal…

Pre-process…Dilation, Erosion, Closing …

Pre-process…Adjustment, Enhancement …

Localization…Filling,Morphological Operations..

Connected Component Analysis, Segmentation & OCR….

• Detection of Connected Components using regionprops function in MATLAB.

• Calculation of Bounding boxes and its dimensions.

• Finding out the required Boundingboxes using histograms taking Y-axis coordinates and Y-axis width to the criterions to met.

• Finding out ‘required’ BoundingBoxes using Takeboxes and Guess functions as implemented in code.

• Template Matching to read and display characters in a text file.

Output…

Challenges…

There are a number of possible difficulties that the software must be able to cope with. The issues can be broadly categorised into two:

• Standardization• Image Quality

StandardizationEven though there is a proper standard for licence plates in India, people are neither worried nor bothered about this, which stands as the greatest challenge faced. They vary in dimensions, fonts(type and size), colours and position of the plate. We may also find artworks which make it difficult for recognition.

Image QualityThe image quality depends on the camera resolution and lighting. Undesirable blobs (like screws and holograms) may also creep in which increases complexity in the character recognition phase.

Future Work…

Depending on the interest of the team, we can look forward to working and improving on our current project by working on the following challenges :

• Standardization• Plate Orientation• Plate Localization• Improving low quality image processing• Realtime Processing

Bibliography…