Improving patch-based scene text script identification with ensembles of conjoined networks

PATTERN RECOGNITION

卷:67

Article

Gomez, Lluis¹  Nicolaou, Anguelos¹  Karatzas, Dimosthenis¹ 

[1] Univ Autonoma Barcelona, Comp Vis Ctr, Edifici O,Campus UAB, Barcelona 08193, Cerdanyola, Spain

关键词: Script identification;    Scene text understanding;    Multi-language OCR;    Convolutional neural networks;    Ensemble of conjoined networks;   

DOI  :  10.1016/j.patcog.2017.01.032

来源: Elsevier

【 摘 要 】

This paper focuses on the problem of script identification in scene text images. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed aspect ratio as in the typical use of holistic CNN classifiers, we propose here a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. Our experiments with this learning procedure demonstrate state-of-the-art results in two public script identification datasets. In addition, we propose a new public benchmark dataset for the evaluation of multi-lingual scene text end-to-end reading systems. Experiments done in this dataset demonstrate the key role of script identification in a complete end-to-end system that combines our script identification method with a previously published text detector and an off-the-shelf OCR engine. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free