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In this paper, we propose DEXTER, an end to end system to extract informationfrom tables present in medical health documents, such as electronic healthrecords (EHR) and explanation of benefits (EOB). DEXTER consists of foursub-system stages: i) table detection ii) table type classification iii) celldetection; and iv) cell content extraction. We propose a two-stage transferlearning-based approach using CDeC-Net architecture along with Non-Maximalsuppression for table detection. We design a conventional computer vision-basedapproach for table type classification and cell detection using parameterizedkernels based on image size for detecting rows and columns. Finally, we extractthe text from the detected cells using pre-existing OCR engine Tessaract. Toevaluate our system, we manually annotated a sample of the real-world medicaldataset (referred to as Meddata) consisting of wide variations of documents (interms of appearance) covering different table structures, such as bordered,partially bordered, borderless, or coloured tables. We experimentally show thatDEXTER outperforms the commercially available Amazon Textract and MicrosoftAzure Form Recognizer systems on the annotated real-world medical dataset

DEXTER: An end-to-end system to extract table contents from electronic medical health documents