  Handwritten Text Recognition (HTR) is more interesting and challenging than
printed text due to uneven variations in the handwriting style of the writers,
content, and time. HTR becomes more challenging for the Indic languages because
of (i) multiple characters combined to form conjuncts which increase the number
of characters of respective languages, and (ii) near to 100 unique basic
Unicode characters in each Indic script. Recently, many recognition methods
based on the encoder-decoder framework have been proposed to handle such
problems. They still face many challenges, such as image blur and incomplete
characters due to varying writing styles and ink density. We argue that most
encoder-decoder methods are based on local visual features without explicit
global semantic information.
  In this work, we enhance the performance of Indic handwritten text
recognizers using global semantic information. We use a semantic module in an
encoder-decoder framework for extracting global semantic information to
recognize the Indic handwritten texts. The semantic information is used in both
the encoder for supervision and the decoder for initialization. The semantic
information is predicted from the word embedding of a pre-trained language
model. Extensive experiments demonstrate that the proposed framework achieves
state-of-the-art results on handwritten texts of ten Indic languages.
