  The graph neural networks has been proved to be an efficient machine learning
technique in real life applications. The handwritten recognition is one of the
useful area in real life use where both offline and online handwriting
recognition are required. The chain code as feature extraction technique has
shown significant results in literature and we have been able to use chain
codes with graph neural networks. To the best of our knowledge, this work
presents first time a novel combination of handwritten trajectories features as
chain codes and graph neural networks together. The handwritten trajectories
for offline handwritten text has been evaluated using recovery of drawing
order, whereas online handwritten trajectories are directly used with chain
codes. Our results prove that present combination surpass previous results and
minimize error rate in few epochs only.
