 A novel boot strapping algorithm for text extraction in a self-organising neural network modelXiaohong Li and Maolin Li International Journal of Networking and Virtual Organisations, 2019, vol. 21, issue 1, 63-75 Abstract: With rapid growth in internet and its associated communication protocols, need for printed documents to be carried over from one place to another has been reduced to minimise the cost and time. Research contributions in the past have paved the way for implementation of smart and intelligent algorithms to further minimise manual intervention in processing of documents. One such area is the automation of text extraction from documents with increased accuracy and least number of false detections. A wide range of algorithms and methodologies have been reported in the past towards efficient extraction of text from documents which may be online or offline. This research paper proposes an efficient extraction algorithm of text from given set of documents which may or may not be graphic through utilisation of a hybrid SOM-ANN algorithm. The experimentation has been done over a wide variety of inputs and convergence of error in extraction is found to be minimum when compared to other conventional extractors. Keywords: extraction algorithms; intelligent extractors; neural networks; self-organising map; SOM; bootstrapping. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijnvor:v:21:y:2019:i:1:p:63-75 Access Statistics for this article More articles in International Journal of Networking and Virtual Organisations from Inderscience Enterprises Ltd |
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