Text detection from natural scene images: towards a system for visually impaired persons

Abstract

We propose a system that reads the text encountered in natural scenes with the aim to provide assistance to the visually impaired persons. This paper describes the system design and evaluates several character extraction methods. Automatic text recognition from natural images receives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. Camera-based document analysis becomes a real possibility with the increasing resolution and availability of digital cameras. However, in the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. At first, our system tries to find in the image areas with small characters. Then it zooms into the found areas to retake higher resolution images necessary for character recognition. In t he present paper, we propose four character-extraction methods based on connected components. We tested the effectiveness of our methods on the ICDAR 2003 Robust Reading Competition data. The performance of the different methods depends on character size. In the data, bigger characters are more prevalent and the most effective extraction method proves to be the sequence: Sobel edge detection, Otsu binarization, connected component extraction and rule-based connected component filtering.

Study on Accessible Long-Term Care Mobile Application for Visually Impaired Users

“…That makes them be able to tell accompanying difficulties with empathy. Therefore, from questionnaires, we can find out visually impaired people's needs in the angle of these companions [3,4].…”

Text Particles Multi-band Fusion for Robust Text Detection

“…2). connected component (CC) based method [4][5], in which connected component of text regions are detected and extracted as descriptors, which are simple to implement for text localization. 3).…”

“…Recent years, there is a hot topic about the multimedia content analysis, retrieval and annotation [1][2][3][4][5][6][7][8][9]. Comparing with other visual contents, text information extracted from images/videos is near to its high-level semantic cues.…”

“…In the state-of-the-art methods [2][3][4][5][6][7][8], two problems are not well solved, which strictly restrict text detection performance of the text detection algorithms in realworld applications:…”

“…When the text region is similar to background ( Fig. 1 (a)), it is difficult to get high performance using either color or texture threshold based methods [4][5][6][7][8] due to the low contrast. 2.…”