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We propose a new approach to extracting data items or field values fromsemi-structured documents. Examples of such problems include extractingpassenger name, departure time and departure airport from a travel itinerary,or extracting price of an item from a purchase receipt. Traditional approachesto data extraction use machine learning or program synthesis to process thewhole document to extract the desired fields. Such approaches are not robust toformat changes in the document, and the extraction process typically fails evenif changes are made to parts of the document that are unrelated to the desiredfields of interest. We propose a new approach to data extraction based on theconcepts of landmarks and regions. Humans routinely use landmarks in manualprocessing of documents to zoom in and focus their attention on small regionsof interest in the document. Inspired by this human intuition, we use thenotion of landmarks in program synthesis to automatically synthesize extractionprograms that first extract a small region of interest, and then automaticallyextract the desired value from the region in a subsequent step. We haveimplemented our landmark-based extraction approach in a tool LRSyn, and showextensive evaluation on documents in HTML as well as scanned images of invoicesand receipts. Our results show that our approach is robust to various types offormat changes that routinely happen in real-world settings.

Landmarks and Regions: A Robust Approach to Data Extraction