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Offline Handwritten Text Recognition using Deep Learning

Phumudzo Neluhni English

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Offline Handwritten Text Recognition using Deep Learning

  1. Review literature offered of various sorts of alphabetical detection systems that can be deployed so that they can carry out handwritten CRS.

    • Figure out strategies that are most appropriate and are efficient in regards to consumed resources and time, therefore justifying their execution.
      • develop an algorithm for that acknowledges hand-written alphabetical symbols and works well in any given circumstance as well as laboratory environments and ideally in a comparatively low time-frame.
  2. conduct assessments in effect for ascertaining the accuracy alongside performance of suggested designs and state additional studies that can be conducted so as to boost the accuracy and performance.

    • To develop hand-written character recognition system by utilizing an image processing tool box
      • To carry out tests on devised algorithms on varied test pictures and consequently determine problems within the created algorithm. Assessment is to be based on success rate as well as time consumed and the algorithm’s capability to functions in various circumstances.

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Mind Map on Offline Handwritten Text Recognition using Deep Learning, created by Phumudzo Neluhni on 07/08/2018.
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