MC Enhanced Inhouse OCR Engine

Enhanced Inhouse OCR Engine

 

Executive Summary
  • A mobile device captures images of metallic tags/plates
  • The serial number engrossed/embossed on the tags has to be read via OCR.
Requirements
  • Build NATIVE(for OCR) APP iOS7+ platform
  • APP will support only «English» language and English alphanumeric characters on a data plate
  • Printed, engrained, embossed data plates
  • After taking picture, with drag method, a rectangle to be drawn on the area of interest.
  • Adapt and extend open source OCR library to meet the recognition and conversion of data plate –serial number(picture to text)
  • Solution will work for reading «Data plate» for competition equipment, so it is future proof
  • While picture is taken, APP will record its GPS location or GSM location
Challenges
  • Engraved/embossed images/metallic tags are low in contrast which becomes difficult for OCR to detect.
  • Images taken with flash on or very bright surface shine gives false result.
  • If too much extra area is selected, due to noise OCR recognizes false characters.
  • Characters connected with each other is difficult to process for OCR.
  • As locational logic (like vehicle license plates) is not applicable in alpha numeric strings of text, the similar appearance between numbers and characters like I & 1, 5 & S, O & 0, B & 8 etc. may lead to wrong recognition and would not be considered for success rate percentage
Solutions
  • Development of an image rejection module which checks image quality and provide feedback to recapture image.
  • Automatic skew detection and correction if text having some rotation.
  • Flashy effect removal using homomorphic filter.
  • Line removal, if text have some small linkage with line.
  • Automatic text categorization into white and black text using image processing techniques.
  • Separate image pre-processing modules considering black and white text.
  • Enhanced OCR engine for specific metallic images.

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