Evaluating the use of OCR on a Mobile Device Presented by Hamed Alharbi Supervisor by Dr Brett Wilkinson

Introduction Experiment Type of mobile device Distance Time Result Most Common Interpretation Errors Experimental results indicate accuracy for OCR Process Variation of Plates Issues with capture Conclusion and Future Work Outline

The project idea To implement an application on the mobile to capture an image of the license number Translate this image to a valid text through optical character recognition Use this translated number to search in database to get some information about the license Introduction

Experiment “80 cm, 100 cm, and 150 cm”.  The practical experiment that I have executed was using From 8 am to 12 pm, from 12 pm to 3 pm, and from 3 pm to 6 pm.  Experiment is executed in 3 different time slots a Samsung Galaxy Ace mobile S5830V 5 MP camera Android OS version 2.3.  The experiment is executed with 3 different distances

Distance 80CM Time OCR Accuracy 83% 75% Findings  Best accuracy was in period from 3pm to 6 pm  Worst accuracy was in period from 12 pm to 3 pm  Average accuracy was in period from 8 am to 12 pm Application accuracy for 80 cm distance for 3 different time slots 87%

Distance 100CM Time OCR Accuracy 81% 76% 85% Findings  Best accuracy was in period from 3pm to 6 pm.  Worst accuracy was in period from 12 pm to 3 pm.  Average accuracy was in period from 8 am to 12 pm Application accuracy for 100 cm distance for 3 different time slots

Distance 150CM Time OCR Accuracy 72% 71% 80% Findings  Best accuracy was in period from 3pm to 6 pm  Worst accuracy was in period from 12 pm to 3 pm  Average accuracy was in period from 8 am to 12 pm Application accuracy for 150 cm distance for 3 different time slots Note  That over all accuracy is low for distance 150 cm.

Flash Headlight glare Distance OCR Accuracy 63% 57% Findings  Using flash lowers the application accuracy  80 cm distance produces better accuracy than 50 cm Application accuracy for flash for 2 different distances Note  Using flash make over all application accuracy very low

RESULT Distance OCR Accuracy 83% 74% 63% 57% Findings  Best accuracy was in cases of 80 cm and 100 cm  Worst accuracy was in case of using flash at 50 cm distance Summary for application accuracy in different conditions 84%

Most Common Interpretation Errors CorrectGenerated CE, F 2Z.=, I, <, “,% J] Z2 S5 AI YV “ 8B 5S 9Q,g O0 Findings  Some characters looks very similar to other characters or litters “Z looks like 2, O looks like 0”  Font type has impact to the interpretation errors Table 1 :Popular interpretation errors S S B B Table 2:Most occurring interpretation errors One of expected difficulties  font type of text  Mobile camera quality

Experimental results indicate accuracy for OCR No of Images No of characters No of error/s Distance _headlight glare PLATE NUMER CANNOT CONTAIN MORE THAN 7 CHARATERS TotalNo of characters SCANNED = No of image * 7 Table1:contains total number of images captured for each distance Notes  Each image contains 7 characters  Minimum error factor was in 80cm distance  

Process

Remote Database Query NO YES

Process Notes  Database server is Microsoft Access Database  Mobile application will NOT communicate directly to database server.  Web server is used as intermediate layer between mobile application and database server  Mobile application communicates with web server through HTTP request/response

Variation of Plates Findings  There are many types of plate numbers  Application is tested for many types of plates with different background colours and text colours.  Application accuracy is NOT impacted by plate number background colour, text colour, or font size Table 1 :Different types of plates

Issues with capture Table 1 :Examples of poorly captured plates Expected difficulties Image is distorted  Because of: Motion Image has shades Image has reflation Image not clear due plate is damage Image not clear because plate is dirty

Conclusion and Future Work  Conclusion The main idea of the project is explained, and detailed process is demonstrated, and experiment results is compared. Difficulties faced are discussed, and various factors that impacted the application accuracy are explained in details.  Future work and enhancements Application may support live video feed instead of image capture and save, this will make application easier to be used, and will lead to faster process.

Questions ?