  A handwritten word recognition system comes with issues such as lack of large
and diverse datasets. It is necessary to resolve such issues since millions of
official documents can be digitized by training deep learning models using a
large and diverse dataset. Due to the lack of data availability, the trained
model does not give the expected result. Thus, it has a high chance of showing
poor results. This paper proposes a novel way of generating diverse handwritten
word images using handwritten characters. The idea of our project is to train
the BiLSTM-CTC architecture with generated synthetic handwritten words. The
whole approach shows the process of generating two types of large and diverse
handwritten word datasets: overlapped and non-overlapped. Since handwritten
words also have issues like overlapping between two characters, we have tried
to put it into our experimental part. We have also demonstrated the process of
recognizing handwritten documents using the deep learning model. For the
experiments, we have targeted the Bangla language, which lacks the handwritten
word dataset, and can be followed for any language. Our approach is less
complex and less costly than traditional GAN models. Finally, we have evaluated
our model using Word Error Rate (WER), accuracy, f1-score, precision, and
recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92%
percent f1 scores using non-overlapped data and 63% percent WER score, 83%
percent accuracy, and 85% percent f1 scores using overlapped data.
