This paper aims to study the problem of text recognition in complex natural scenes. Now the rapid development of technology makes most of the industry need fast and efficient text recognition tools, such as the logistics industry and publishers. Although traditional OCR technology can solve most simple background text recognition problems, the performance of complex scenes is somewhat unsatisfactory. In this paper, the focus is to extract the text in complex scenes accurately. The algorithm of stroke width transform and Convolution Neural Network is proposed. Using the stroke width transform algorithm for text positioning; And then separate the text line, end to the classifier to identify, through the recognition of the correct rate determine the effectiveness of the algorithm. We evaluate the proposed approach on a widely used dataset. Results show that our method achieves desired result compared with the state-of-the-art methods of the participant team which clearly demonstrate its competency.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.