LED Dot matrix text recognition method in natural scene

摘要

In recent years, light-emitting diodes dot-matrix text (LED text) is being widely used for displaying information and announcements. However, there is currently no text detection system that is capable of handling LED text. Unlike general printed text, it is not easy to detect and recognize LED text due to its discontinuity. A character of the LED is generally displayed with a matrix of segments and composed with them to generate the text. Furthermore, it is necessary to detect each character from a line of LED text for creating a robust text detection system. Thus, this paper proposes a method for LED text detection and recognition in natural scene images. To perform this goal of detection and recognition of a character and text, it consists of two main steps with the following steps: the first step, a Canny edge was used to detect character pixels which appear in LED display area from scene images. The center points of edge segments are calculated. These points are merged based on their properties to generate a character candidate. In order to obtain character feature, the spatial information such as a centroid and orientation of the character candidate are used. These values are then analyzed using a k-nearest neighbor approach for classifying the character candidate as a certain alphanumeric. In the second step, the recognized characters are later combined into a text line based on the similarity of their characteristics such as width, height, aspect ratio and color. The post-processing of text line generating is then applied for rectifying the falsely recognized characters. In experiments, our proposed method achieves 68.8% and 47% for detection and recognition rate, respectively. These results show the robustness and effectiveness of the proposed method for detecting and recognizing the LED text in natural scene images that has filled the vacancy that the printed and dense text detection system has not covered. (C) 2014 Elsevier B.V. All rights reserved.