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Automated Supply-Chain Quality Inspection Using Image Analysis and Machine Learning
Kristianstad University, Faculty of Natural Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

An image processing method for automatic quality assurance of Ericsson products is developed. The method consists of taking an image of the product, extract the product labels from the image, OCR the product numbers and make a database lookup to match the mounted product with the customer specification. The engineering innovation of the method developed in this report is that the OCR is performed using machine learning techniques. It is shown that machine learning can produce results that are on par or better than baseline OCR methods. The advantage with a machine learning based approach is that the associated neural network can be trained for the specific input images from the Ericsson factory. Imperfections in the image quality and varying type fonts etc. can be handled by properly training the net, a task that would have been very difficult with legacy OCR algorithms where poor OCR results typically need to be mitigated by improving the input image quality rather than changing the algorithm.

Place, publisher, year, edition, pages
2019. , p. 58
Keywords [en]
Image Analysis, Computer vision, OCR, Machine Learning, Neural Networks, LSTM networks
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hkr:diva-20069OAI: oai:DiVA.org:hkr-20069DiVA, id: diva2:1366169
External cooperation
Ericsson company
Educational program
Master Programme with specialization in Embedded Systems
Supervisors
Examiners
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-10-28Bibliographically approved

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17181920212223 23 of 23
CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf