  Lip-reading is the operation of recognizing speech from lip movements. This
is a difficult task because the movements of the lips when pronouncing the
words are similar for some of them. Viseme is used to describe lip movements
during a conversation. This paper aims to show how to use external text data
(for viseme-to-character mapping) by dividing video-to-character into two
stages, namely converting video to viseme, and then converting viseme to
character by using separate models. Our proposed method improves word error
rate by 4\% compared to the normal sequence to sequence lip-reading model on
the BBC-Oxford Lip Reading Sentences 2 (LRS2) dataset.
