6,335 research outputs found
Lip Reading Sentences in the Wild
The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos.
Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS)
network that learns to transcribe videos of mouth motion to characters; (2) a
curriculum learning strategy to accelerate training and to reduce overfitting;
(3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition,
consisting of over 100,000 natural sentences from British television.
The WLAS model trained on the LRS dataset surpasses the performance of all
previous work on standard lip reading benchmark datasets, often by a
significant margin. This lip reading performance beats a professional lip
reader on videos from BBC television, and we also demonstrate that visual
information helps to improve speech recognition performance even when the audio
is available
AON: Towards Arbitrarily-Oriented Text Recognition
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201
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