  The Handwritten Text Recognition problem has been a challenge for researchers
for the last few decades, especially in the domain of computer vision, a
subdomain of pattern recognition. Variability of texts amongst writers,
cursiveness, and different font styles of handwritten texts with degradation of
historical text images make it a challenging problem. Recognizing scanned
document images in neural network-based systems typically involves a two-step
approach: segmentation and recognition. However, this method has several
drawbacks. These shortcomings encompass challenges in identifying text regions,
analyzing layout diversity within pages, and establishing accurate ground truth
segmentation. Consequently, these processes are prone to errors, leading to
bottlenecks in achieving high recognition accuracies. Thus, in this study, we
present an end-to-end paragraph recognition system that incorporates internal
line segmentation and gated convolutional layers based encoder. The gating is a
mechanism that controls the flow of information and allows to adaptively
selection of the more relevant features in handwritten text recognition models.
The attention module plays an important role in performing internal line
segmentation, allowing the page to be processed line-by-line. During the
decoding step, we have integrated a connectionist temporal classification-based
word beam search decoder as a post-processing step. In this work, we have
extended existing LexiconNet by carefully applying and utilizing gated
convolutional layers in the existing deep neural network. Our results at line
and page levels also favour our new GatedLexiconNet. This study reported
character error rates of 2.27% on IAM, 0.9% on RIMES, and 2.13% on READ-16, and
word error rates of 5.73% on IAM, 2.76% on RIMES, and 6.52% on READ-2016
datasets.
