  In this paper, we propose DEXTER, an end to end system to extract information
from tables present in medical health documents, such as electronic health
records (EHR) and explanation of benefits (EOB). DEXTER consists of four
sub-system stages: i) table detection ii) table type classification iii) cell
detection; and iv) cell content extraction. We propose a two-stage transfer
learning-based approach using CDeC-Net architecture along with Non-Maximal
suppression for table detection. We design a conventional computer vision-based
approach for table type classification and cell detection using parameterized
kernels based on image size for detecting rows and columns. Finally, we extract
the text from the detected cells using pre-existing OCR engine Tessaract. To
evaluate our system, we manually annotated a sample of the real-world medical
dataset (referred to as Meddata) consisting of wide variations of documents (in
terms of appearance) covering different table structures, such as bordered,
partially bordered, borderless, or coloured tables. We experimentally show that
DEXTER outperforms the commercially available Amazon Textract and Microsoft
Azure Form Recognizer systems on the annotated real-world medical dataset
