The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.
FEED FORWARD BACK PROPAGATION NEURAL NETWORK BASED CHARACTER RECOGNITION SYSTEM FOR TAMIL PALM LEAF MANUSCRIPTS
