  In this paper, we introduce a novel technique to recover the pen trajectory
of offline characters which is a crucial step for handwritten character
recognition. Generally, online acquisition approach has more advantage than its
offline counterpart as the online technique keeps track of the pen movement.
Hence, pen tip trajectory retrieval from offline text can bridge the gap
between online and offline methods. Our proposed framework employs sequence to
sequence model which consists of an encoder-decoder LSTM module. Our encoder
module consists of Convolutional LSTM network, which takes an offline character
image as the input and encodes the feature sequence to a hidden representation.
The output of the encoder is fed to a decoder LSTM and we get the successive
coordinate points from every time step of the decoder LSTM. Although the
sequence to sequence model is a popular paradigm in various computer vision and
language translation tasks, the main contribution of our work lies in designing
an end-to-end network for a decade old popular problem in Document Image
Analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit
dataset are used for our experiments. Our proposed method has achieved superior
performance compared to the other conventional approaches.
