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Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via `direct decoding. However, scene text images suffer from rich noises of different sources such as complex background and geometric distortions which often confuse the decoder and lead to incorrect alignment of visual features at noisy decoding time steps. This paper presents I2C2W, a novel scene text recognizer that is accurate and tolerant to various noises in scenes. I2C2W consists of an image-to-character module (I2C) and a character-to-word module (C2W) which are complementary and can be trained end-to-end. I2C detects characters and predicts their relative positions in a word. It strives to detect all possible characters including incorrect and redundant ones based on different alignments of visual features without the restriction of time steps. Taking the detected characters as input, C2W learns from character semantics and their positions to filter out incorrect and redundant detection and produce the final word recognition. Extensive experiments over seven public datasets show that I2C2W achieves superior recognition performances and outperforms the state-of-the-art by large margins on challenging irregular scene text datasets.
Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognit
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Scene text recognition has been an important, active research topic in computer vision for years. Previous approaches mainly consider text as 1D signals and cast scene text recognition as a sequence prediction problem, by feat of CTC or attention bas
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Scene text recognition models have advanced greatly in recent years. Inspired by human reading we characterize two important scene text recognition models by measuring their domains i.e. the range of stimulus images that they can read. The domain spe