ocr-nlp-flyer

Top 6 Submission at 2020 Daisy Hackathon @ UofT

Solution

The program utilizes OpenCV to process and segment the flyer image into bounding boxes for each product. PyTesseract, a LSTM enabled image-to-text engine, then extracts characters from each box, and the text is then parsed by empirical methods such as RegEx to extract specific pricing, discount, product name, and organic-status information.

Problem Scope

• Goal: Given high-resolution flyer image, the objective is to return a table of products and their accompanying promotion details, including:
  • Product Name
  • Promo. Price ($)
  • Unit of measure
  • Least unit for promo.
  • Amount saved per unit ($)
  • Discount (%)
  • Organic product (Boolean)
• Data: 212 Flyer Images w/ no labels, Product + Unit-of-Measure Dictionary