Abstract: Using a convolutional neural network, a method for digitizing and extracting meaning from graphic objects such as bar and pie charts, decomposes a chart into its sub-parts (pie and slices or bars, axes and legends) with significant tolerance to the wide range of variations in shape and relative position of pies, bars, axes and legends. A linear regression calibration allows properly reading values even when there are many OCR failures.
Type:
Grant
Filed:
May 17, 2018
Date of Patent:
July 28, 2020
Assignee:
Tab2ex LLC
Inventors:
Paolo Messina, Vincenzo Del Zoppo, Salvatore Messina, Danilo Giuffrida
Abstract: Using a convolutional neural network, a method for digitizing and extracting meaning from graphic objects such as bar and pie charts, decomposes a chart into its sub-parts (pie and slices or bars, axes and legends) with significant tolerance to the wide range of variations in shape and relative position of pies, bars, axes and legends. A linear regression calibration allows properly reading values even when there are many OCR failures.
Type:
Application
Filed:
May 17, 2018
Publication date:
November 22, 2018
Applicant:
TAB2EX, LLC
Inventors:
Paolo Messina, Vincenzo Del Zoppo, Salvatore Messina, Danilo Giuffrida