Using a neural network for industrial character recognition

Clicks: 2
ID: 286718
1993
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Abstract
The pattern classification abilities of neural networks make them suitable for practical image recognition tasks such as industrial character recognition. In this thesis, backpropagation trained multi-layer networks applied to recognition of IC characters are investigated with the aim of ascertaining the network sizes that are suitable for both rotated and unrotated characters, and the performance of these networks with untrained font types. To avoid a huge combinatorial explosion of possibilities to explore, a single method for preprocessing and representing character data was used for all the networks. Characters also consisted only of digits to limit training time. A significant feature in all the training sessions was the exclusion of actual IC character images in the training sets. This was to support the objective of determining the extent of font type invariance of backpropagation networks. Despite this, 100 percent recognition of the test ICs was still possible in one case. Lastly, it is emphasized that the results of the investigation are conclusive within the parameters of this investigation.
Reference Key
persistent_1760659434_68f187ea11f07 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pinpin, Lord Kenneth M.
Journal Malay Journal
Year 1993
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etd_masteral/1519
Keywords Keywords not found

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