Handwriting recognition using webcam for data entry

The Handwriting Recognition using Webcam for Data Entry project has its
primary purpose to develop a system or algorithm that is robust enough to recognize
numerical handwritings. A web camera is to be utilized to capture images of handwritten
scores and question numbers on the examination score sheet in real time. It is then
preprocessed and all the features are being fed into a neural network that is already been
trained by various test samples. The outcome of the project should be able to obtain a
system that is able to recognize handwritten numerical data with the lowest overshoot and
errors. Several distinctive feature from each character is extracted using a few feature
extraction methods, in which a comparison between three types of feature extraction
modules were used. The first test was done with a neural network trained with only the
Character Vector Module as its feature extraction method. A result that is far below the
set point of the recognition accuracy was achieved, a mere average of 64.67% accuracy.
However, the testing were later enhanced with another feature extraction module, which
consists of the combination of Character Vector Module, Kirsch Edge Detection Module,
Alphabet Profile Feature Extraction Module, Modified Character Module and Image
Compression Module. The modules have its distinct characteristics which is trained using
the Back-Propagation algorithm to cluster the pattern recognition capabilities among
different samples of handwriting. Several untrained samples of numerical handwritten
data were obtained at random from various people to be tested with the program. The
second tests shows far greater results compared to the first test, have yielded an average
of 84.52% accuracy. As the recognition results have not reached the target of 90%, further
feature extraction modules are being recommended and an additional feature extraction
module was added for the third test, which successfully yields 90.67%. With the timeframe
target achieved, a robust data entry system was developed using web camera
together with a user-friendly GUI (Graphical User Interface).