看与听：音频引导的文本识别
Looking and Listening: Audio Guided Text Recognition
AI总结：
本文提出 AudioOCR，利用 mel 频谱图概率音频解码器在训练阶段引导场景文本识别，在不增加推理成本的情况下，于 12 个基准和 7 种方法上实现一致提升，并泛化至非英语、OOV 及口音文本。
摘要：
自然场景中的文本识别是计算机视觉领域一个长期存在的问题。在端到端深度学习的推动下，近期研究表明视觉和语言处理对场景文本识别是有效的。然而，解决添加、删除或替换等编辑错误仍是现有方法面临的主要挑战。事实上，文本内容与其音频天然相互对应，即单个字符错误就可能导致明显不同的发音。本文提出 AudioOCR，这是一种简单而有效的概率音频解码器，用于 mel 频谱图序列预测，以引导场景文本识别；它仅参与训练阶段，在推理阶段不会带来额外成本。AudioOCR 的基本原理可以很容易地应用于现有方法。在 12 个现有的规则、不规则和遮挡基准上使用 7 种既往场景文本识别方法进行的实验表明，我们提出的方法能够带来一致提升。更重要的是，通过实验，我们证明 AudioOCR 具备可扩展到更具挑战性场景的泛化能力，包括识别非英语文本、词表外单词以及带有各种口音的文本。代码将在 https://github.com/wenwenyu/AudioOCR 提供。
英文摘要：
Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In fact, the content of the text and its audio are naturally corresponding to each other, i.e., a single character error may result in a clear different pronunciation. In this paper, we propose the AudioOCR, a simple yet effective probabilistic audio decoder for mel spectrogram sequence prediction to guide the scene text recognition, which only participates in the training phase and brings no extra cost during the inference stage. The underlying principle of AudioOCR can be easily applied to the existing approaches. Experiments using 7 previous scene text recognition methods on 12 existing regular, irregular, and occluded benchmarks demonstrate our proposed method can bring consistent improvement. More importantly, through our experimentation, we show that AudioOCR possesses a generalizability that extends to more challenging scenarios, including recognizing non-English text, out-of-vocabulary words, and text with various accents. Code will be available at https://github.com/wenwenyu/AudioOCR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
