Few-Shot Data Augmentation for Industrial Character Recognition

Abstract

The task of industrial character recognition is to extract character content on the surface of the workpiece in the industrial production process. Limited training data, incomplete available character categories and non-standardized character styles encountered in actual production have led to a significant reduction in the recognition performance of deep learning-based methods, such as scene text recognition and Optical Character Recognition (OCR). In this paper, we propose an augmentation strategy suitable for industrial character recognition based on the Generative Adversarial Network (GAN). The strategy consists of two modules, a character detection module and a synthetic data generation module. The results show that the augmentation strategy achieves best generation results. Recognition network utilizing the augmentation dataset generated by the strategy can achieve the best results on four types of industrial datasets.