Text Detection and Recognition in the Automotive Context

Abstract

This thesis achieved the goal of obtaining high accuracy rates (precision and recall) in a real-time system that detects and recognizes text in the automotive context. For the sake of simplicity, this work targets two Objects of Interest (OOIs): North American (NA) traffic boards (TBs) and license plates (LPs).

The proposed approach adopts a hybrid detection module consisting of a Connected Component Analysis (CCA) step followed by a Texture Analysis (TA) step. An initial set of candidates is extracted by highlighting the Maximally Stable Extremal Regions (MSERs). Each sebsequent step in the CCA and TA steps attempts to reduce the size of the set by filtering out false positives and retaining the true positives. The final set of candidates is fed into a recognition stage that integrates an open source Optical Character Reader (OCR) into the framework by using two additional steps that serve the purpose of minimizing false readings as well as the incurred delays.

A set of of manually taken videos from various regions of Ottawa were used to evaluate the performance of the system, using precision, recall and latency as metrics. The high precision and recall values reflect the proposed approach's ability in removing false positives and retaining the true positives, respectively, while the low latency values deem it suitable for the automotive context. Moreover, the ability to detect two OOIs of varying appearances demonstrates the flexibility that is featured by the hybrid detection module.