The established paradigm in handwriting recognition techniques involves supervised
learning, where training is performed over fully labelled (transcribed) data. In this paper, we …

With the advent of deep neural networks, handwriting recognition systems have recently
achieved remarkable performance. Unfortunately, to achieve high-quality results, these …

We have proposed a deep learning model leveraging train and test time data augmentation
approaches for the problem of offline handwriting recognition. We made a comprehensive …

Recurrent neural networks have achieved outstanding recognition performance for
handwriting identification despite the enormous variety observed across diverse handwriting …

Offline handwriting recognition is a well-known challenging task in the optical character
recognition field due to the difficulty caused by various unconstrained handwriting styles and …

The success of deep learning based models have centered around recent architectures and
the availability of large scale annotated data. In this work, we explore these two factors …

We present a CNN-BiLSTM system for the problem of offline English handwriting
recognition, with extensive evaluations on the public IAM dataset, including the effects of …

Handwritten documents are recently getting more and more publicly available, but searching
efficiently information through them is difficult. Handwritten Text Recognition systems …

Personalization of handwriting recognition is still an understudied area due to the lack of a
comprehensive dataset. We collect a dataset of 37,000 words handwritten by 40 writers that …

Progress in Automated Handwriting Recognition has been hampered by the lack of large
training datasets. Nearly all research uses a set of small datasets that often cause models to …