Multi‐lingual text detection and identification using agile convolutional neural network. (26th May 2021)

• Record Type: Journal Article
• Authors: Yegnaraman, Aparna; Valli, S.
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1803
• Page End: 1826
• Publication Date: 2021-05-26
• Subjects: atrous separable convolution -- complete IoU loss -- multi‐lingual text identification -- non‐maximal suppression -- scene text detection -- You Only Look Once; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12467
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Multi‐lingual scene text detection and identification is a challenging task in today's world due to the prevalence of many digitized multi‐lingual documents, images, and videos. A valuable method for detecting multi‐lingual text from natural scene images is proposed which uses the convolutional neural network, namely, You Only Look Once (YOLOv3) as the backbone. The proposed system is more agile than YOLOv3 with the introduction of atrous separable convolution (ASC). The multi‐scale prediction in YOLOv3 emphasizes the integration of global features of multi‐scale convolutional layers while it overlooks the blend of the multi‐scale local region features on the same convolutional layer. To overcome this, ASC is applied to efficiently compute dense local region feature maps, thereby reducing computation complexity substantially. Complete IoU loss, which is an accumulation of overlap area, distance, and aspect ratio, is introduced for enhanced accuracy in bounding box regression, wherein IoU designates the measure of overlap between the predicted and the ground truth bounding boxes. The experimental results show that the proposed system is efficacious in detecting multi‐lingual as well as English text from natural scene images.