Abstract
Scene text recognition has drawn increasing concerns from the OCR community in recent years. Among numerous methods that have been proposed, local feature based methods represented by bag-of-features (BoFs) show notable robustness and efficiency. However, as the existing detectors are based on assumptions about local saliency, a vast number of non-informative local features would be detected in the feature detection stage. In this paper, we propose to remove non-informative local features by integrating feature scales with stroke width information.Experiments taken both on synthetic data and real scene data show that the proposed feature selection method could effectively filter non-informative features and improve the recognition accuracy.
Highlights
In recent years, scene text recognition (STR) [1] technologies have got increasing concerns from OCR community and other related fields
We focus on local feature based STR and propose a novel criterion which integrate stroke width information with local feature scales to remove noninformative local features and achieve higher accuracy
We propose a novel local feature selection criterion that selects effective local features based on the ratio between character stroke width and local feature scale
Summary
Scene text recognition (STR) [1] technologies have got increasing concerns from OCR community and other related fields. Diem and Sablatnig [10] build a historical document analysis system based on local descriptors and achieve a state-of-art accuracy for ancient character recognition Among these methods, the ones based on local features [6],[7], [10] show notable robustness and effectiveness, especially when in small sample size situations and situations containing image degradations [11]. There should be an appropriate proportion between local feature scale and the corresponding stroke width if these features reflect local text structures such as corner and cross
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call