An Automated Road Sign Recognition system using Artificial Neural Network for the Textual Information box inscribing in Bengali is presented on the paper. Signs are visual languages that represent some special circumstantial information of environment. Road signs, being among the most important around us primarily for safety reasons, are designed, and manufactured and installed according to tight regulations. The system captures real time images every two seconds and saves them as JPG format files. Firstly some road sign are already stored in the memory. Like: Warning Sign, Prohibition Sign, Obligation Sign and Informative Sign. Car Driver concentration and illiterateness isn’t always focused on what it should be and not always notice the road signs. For these reasons, automation of Bangla Road Sign Recognition system is highly essential. Previously several works are done by Mueller, Piccioli, Novovicova, Yuille, Escalera and others. But those are not in Bengali. Real Time Road Sign Recognition System Using Artificial Neural Networks for Bengali Textual Information Box which is done by Mohammad Osiur Rahman, Fouzia Asharf Mousumi, Edgar Scavino, Aini Hussain, Hassan Basri whose are from the Department of Computer Science and Engineering, University of Chittagong, Chittagong-4331, Bangladesh, Faculty of Engineering, University Kebangsaan Malaysia. For doing this they divide the total Concept in Steps: 1. Image Acquisition: From several video sequences from a moving vehicle for a certain period are consecutive frames recorded within 2 seconds are similar. For this they have used Application Programming Interface functions of VB 6.0. Every 2-second a frame is collected and stored in JPG format.
2. Preprocessing: Median filter is used to reduce impulsive or salt-and-pepper type noise from captured images and then normalized into 320 X 240 pixels.
3. Text Detection and Extraction: An algorithm was developed for textual information detection and extraction from Bangla Road Signs on the basis of the Sobel Edge Detection technique. Like the following: I. Read input image in .jpg format
II. Convert colored image into gray scale image
III. Apply 3x3 median filter convolution masks on gray scale image IV. Calculated edges by applying Sobel convolutions mask
V. Thicken the calculated edges by dilation
VI. Apply vertical Sobel projection filter on dimmed image VII. Create a histogram by computing projection values
VIII. Find the threshold value of the image
IX. Loop on the possible positive identifications based on the histogram values X. Extract the possible positive identifications based on the histogram values XI. Apply Sobel horizontal edge-emphasis for other possible text area searches XII. Convert detected text region into binary image
XIII. Calculate height and width of detected region of text
XIV. Crop the image
4. Bangla OCR using MLP: An ANN based approach is used for Bangla OCR of road signs’ text. It has 3 sub modules – Character segmentation, Feature Extraction and Character Recognition by MLP NN.
5. Confirmation of Textual Road Signs and Conversion
6. Speech synthesis
The Proposed system works like the following:
1. From video sequences capture a single frame in JPG format in each 2 seconds. 2. Preprocess the captured image each time
3. Detect the Text and Extract that and then Extracted Text will recognize by Bengali Optical Character Recognition System. 4. Recognized characters of textual information compared with the stored knowledge and then give decision valid or invalid. 5. If Valid then recognize and according to users choice it provide Bengali or it convert to English and provide audio stream.
The system processes the images to find out whether they contain images of road signs or not. The textual information of the...