  Business documents come in a variety of structures, formats and information
needs which makes information extraction a challenging task. Due to these
variations, having a document generic model which can work well across all
types of documents and for all the use cases seems far-fetched. For
document-specific models, we would need customized document-specific labels. We
introduce DoSA (Document Specific Automated Annotations), which helps
annotators in generating initial annotations automatically using our novel
bootstrap approach by leveraging document generic datasets and models. These
initial annotations can further be reviewed by a human for correctness. An
initial document-specific model can be trained and its inference can be used as
feedback for generating more automated annotations. These automated annotations
can be reviewed by human-in-the-loop for the correctness and a new improved
model can be trained using the current model as pre-trained model before going
for the next iteration. In this paper, our scope is limited to Form like
documents due to limited availability of generic annotated datasets, but this
idea can be extended to a variety of other documents as more datasets are
built. An open-source ready-to-use implementation is made available on GitHub
https://github.com/neeleshkshukla/DoSA.
