A fine-grained approach to scene text script identification
This addresses the problem of multi-lingual text understanding in unconstrained environments for computer vision systems, representing an incremental advance in a previously underexplored area.
The paper tackles script identification in natural scene text images by proposing a method combining convolutional features with Naive-Bayes Nearest Neighbor classifier, achieving state-of-the-art results on a new benchmark dataset while generalizing well to other datasets.
This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.