AEMF: An Attention-Based Efficient and Multifeature Fast Text Detector

Abstract

The label from industrial commodity packaging usually contains important data, such as production date, manufacturer, and other commodity-related information. As such, those labels are essential for consumers to purchase goods, help commodity supervision, and reveal potential product safety problems. Consequently, packaging label detection, as the prerequisite for product label identification, becomes a very useful application, which has achieved promising results in the past decades. Yet, in complex industrial scenarios, traditional detection methods are often unable to meet the requirements, which suffer from many problems of low accuracy and efficiency. In this paper, we propose a multifeature fast and attention-based algorithm using a combination of area suggestion and semantic segmentation. This algorithm is an attention-based efficient and multifeature fast text detector (termed AEMF). The proposed approach is formed by fusing segmentation branches and detection branches with each other. Based on the original algorithm that can only detect text in any direction, it is possible to detect different shapes with a better accuracy. Meanwhile, the algorithm also works better on long-text detection. The algorithm was evaluated using ICDAR2015, CTW1500, and MSRA-TD500 public datasets. The experimental results show that the proposed multifeature fusion with self-attention module makes the algorithm more accurate and efficient than existing algorithms. On the MSRA-TD500 dataset, the AEMF algorithm has an F-measure of 72.3% and a frame per second (FPS) of 8. On the CTW1500 dataset, the AEMF algorithm has an F-measure of 62.3% and an FPS of 23. In particular, the AEMF algorithm has achieved an F-measure of 79.3% and an FPS of 16 on the ICDAR2015 dataset, demonstrating the excellent performance in detecting label text on industrial packaging.