Skip to main content

Wafer Identification Recognition by Stroke Analysis and Template Matching

Buy Article:

$105.00 plus tax (Refund Policy)

Wafer ID recognition is one of the problems of optical character recognition (OCR). However, due to some unique characteristics, such as different font sizes and styles, the irregular space between adjacent characters, it will not obtain a good result to recognize wafer ID by directly utilizing traditional OCR approaches. Thus, a wafer ID recognition method using stroke analysis and template matching is developed. The wafer ID images are extracted from the binarized images containing wafer ID objects inscribed by laser light in various fonts. Our method performs statistical analysis on stroke patterns obtained from subimages. Experiments are carried out with 1,308 character images in different styles of characters. On average, a recognition accuracy of 99.39% is achieved.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: 2012-05-01

More about this publication?
  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more