  The paper presents a novel technique called "Structural Crossing-Over" to
synthesize qualified data for training machine learning-based handwriting
recognition. The proposed technique can provide a greater variety of patterns
of training data than the existing approaches such as elastic distortion and
tangent-based affine transformation. A couple of training characters are
chosen, then they are analyzed by their similar and different structures, and
finally are crossed over to generate the new characters. The experiments are
set to compare the performances of tangent-based affine transformation and the
proposed approach in terms of the variety of generated characters and percent
of recognition errors. The standard MNIST corpus including 60,000 training
characters and 10,000 test characters is employed in the experiments. The
proposed technique uses 1,000 characters to synthesize 60,000 characters, and
then uses these data to train and test the benchmark handwriting recognition
system that exploits Histogram of Gradient (HOG) as features and Support Vector
Machine (SVM) as recognizer. The experimental result yields 8.06% of errors. It
significantly outperforms the tangent-based affine transformation and the
original MNIST training data, which are 11.74% and 16.55%, respectively.
