Temporally-aware Convolutional Block Attention Module for Video Text Detection

Abstract

Scene-text spotting is a task that predicts a text area on natural scene images and recognizes its text characters simultaneously. It has attracted much attention in recent years due to its wide applications. Existing research has mainly focused on improving text region detection, not text recognition. Thus, while detection accuracy is improved, the end-to-end accuracy is insufficient. Texts in natural scene images tend to not be a random string of characters but a meaningful string of characters, a word. Therefore, we propose adversarial learning of semantic representations for scene text spotting (A3S) to improve end-to-end accuracy, including text recognition. A3S simultaneously predicts semantic features in the detected text area instead of only performing text recognition based on existing visual features. Experimental results on publicly available datasets show that the proposed method achieves better accuracy than other methods.

Video Sparse Transformer With Attention-Guided Memory for Video Object Detection

“…The aim of text recognition, also known as optical character recognition (OCR), is to convert the text in images into digital text sequences. Many studies have been conducted on this technology owing to its wide range of real-world applications, including reading license plates and handwritten text, analyzing documents such as receipts and invoices [23,58], and analyzing road signs in automated driving and natural scenes [14,16]. However, the various fonts, lighting variations, complex backgrounds, low-quality images, occlusion, and text deformation make text recognition challenging.…”