  Deep Learning (DL) is dominating the fields of Natural Language Processing
(NLP) and Computer Vision (CV) in the recent times. However, DL commonly relies
on the availability of large data annotations, so other alternative or
complementary pattern-based techniques can help to improve results. In this
paper, we build upon Key Information Extraction (KIE) in purchase documents
using both DL and rule-based corrections. Our system initially trusts on
Optical Character Recognition (OCR) and text understanding based on entity
tagging to identify purchase facts of interest (e.g., product codes,
descriptions, quantities, or prices). These facts are then linked to a same
product group, which is recognized by means of line detection and some grouping
heuristics. Once these DL approaches are processed, we contribute several
mechanisms consisting of rule-based corrections for improving the baseline DL
predictions. We prove the enhancements provided by these rule-based corrections
over the baseline DL results in the presented experiments for purchase
documents from public and NielsenIQ datasets.
