  Key value pair (KVP) extraction or Named Entity Recognition(NER) from
visually rich documents has been an active area of research in document
understanding and data extraction domain. Several transformer based models such
as LayoutLMv2, LayoutLMv3, and LiLT have emerged achieving state of the art
results. However, addition of even a single new class to the existing model
requires (a) re-annotation of entire training dataset to include this new class
and (b) retraining the model again. Both of these issues really slow down the
deployment of updated model. \\ We present \textbf{ProtoNER}: Prototypical
Network based end-to-end KVP extraction model that allows addition of new
classes to an existing model while requiring minimal number of newly annotated
training samples. The key contributions of our model are: (1) No dependency on
dataset used for initial training of the model, which alleviates the need to
retain original training dataset for longer duration as well as data
re-annotation which is very time consuming task, (2) No intermediate synthetic
data generation which tends to add noise and results in model's performance
degradation, and (3) Hybrid loss function which allows model to retain
knowledge about older classes as well as learn about newly added classes.\\
Experimental results show that ProtoNER finetuned with just 30 samples is able
to achieve similar results for the newly added classes as that of regular model
finetuned with 2600 samples.
