Strokelets: A Learned Multi-Scale Mid-Level Representation for Scene Text Recognition

In this paper, we are concerned with the problem of automatic scene text recognition, which involves localizing and reading characters in natural images. We investigate this problem from the perspective of representation and propose a novel multi-scale representation, which leads to accurate, robust character identification and recognition. This representation consists of a set of mid-level primitives, termed strokelets, which capture the underlying substructures of characters at different granularities. The Strokelets possess four distinctive advantages: 1) usability: automatically learned from character level annotations; 2) robustness: insensitive to interference factors; 3) generality: applicable to variant languages; and 4) expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of the strokelets and demonstrate the effectiveness of the text recognition algorithm built upon the strokelets. Moreover, we show the method to incorporate the strokelets to improve the performance of scene text detection.