Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 8, Pages 3676-3690Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2825107
Keywords
Scene text detection; multi-oriented text; word spotting; scene text recognition; convolutional neural networks
Funding
- National Natural Science Foundation of China [61733007, 61573160]
- National Program for Support of Top-Notch Young Professionals
- Program for HUST Academic Frontier Youth Team
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Scene text detection is an important step of scene text recognition system and also a challenging problem. Different from general object detections, the main challenges of scene text detection lie on arbitrary orientations, small sizes, and significantly variant aspect ratios of text in natural images. In this paper, we present an end-to-end trainable fast scene text detector, named TextBoxes++, which detects arbitraryoriented scene text with both high accuracy and efficiency in a single network forward pass. No post-processing other than efficient non-maximum suppression is involved. We have evaluated the proposed TextBoxes++ on four public data sets. In all experiments, TextBoxes++ outperforms competing methods in terms of text localization accuracy and runtime. More specifically, TextBoxes++ achieves an f-measure of 0.817 at 11.6 frames/s for 1024 x 1024 ICDAR 2015 incidental text images and an f-measure of 0.5591 at 19.8 frames/s for 768 x 768 COCO-Text images. Furthermore, combined with a text recognizer, TextBoxes++ significantly outperforms the state-of-the- art approaches for word spotting and end-to-end text recognition tasks on popular benchmarks.
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