Traffic Signboard Recognition and Text Translation System using Word Spotting and Machine Learning

Abstract

This project will help the non-native people of Karnataka to easily understand the kannada boards and travel easily. The main task of this work is to recognize the kannada traffic text boards and translate that to English language. Histogram equalization is used to find the gap between the characters. K-means clustering is used to divide the characters into different clusters then the segmented characters are passed to the pretrained model to recognize what the characters means. The model used for recognizing the traffic text is convolutional neural networks. The methodologies used here is the image augmentation, converting RGB image to grey scale and normalizing the image to reduce the noise. The validation accuracy obtained while training the model with coloured images, normalized image, grey scale image and normalized grey scale image is respectively 98.88%, 98.85%, 98.8% and 99.39% and while testing this model with kannada language, the testing accuracy obtained respectively with coloured images, normalized image, grey scale and normalized grey scale image is 95.91%, 96.58%, 95.42% and 96.98 % . In this work, word spotting method is employed for kannada language recognition. The performance of this system is faster since machine learning algorithms are used here.