  In this study, we have presented an efficient procedure using two
state-of-the-art approaches from the literature of handwritten text recognition
as Vertical Attention Network and Word Beam Search. The attention module is
responsible for internal line segmentation that consequently processes a page
in a line-by-line manner. At the decoding step, we have added a connectionist
temporal classification-based word beam search decoder as a post-processing
step. In this study, an end-to-end paragraph recognition system is presented
with a lexicon decoder as a post-processing step. Our procedure reports
state-of-the-art results on standard datasets. The reported character error
rate is 3.24% on the IAM dataset with 27.19% improvement, 1.13% on RIMES with
40.83% improvement and 2.43% on the READ-16 dataset with 32.31% improvement
from existing literature and the word error rate is 8.29% on IAM dataset with
43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on
READ-2016 dataset with 47.27% improvement from the existing results. The
character error rate and word error rate reported in this work surpass the
results reported in the literature.
