  This paper presents a hand-written character recognition comparison and
performance evaluation for robust and precise classification of different
hand-written characters. The system utilizes advanced multilayer deep neural
network by collecting features from raw pixel values. The hidden layers stack
deep hierarchies of non-linear features since learning complex features from
conventional neural networks is very challenging. Two state of the art deep
learning architectures were used which includes Caffe AlexNet and GoogleNet
models in NVIDIA DIGITS.The frameworks were trained and tested on two different
datasets for incorporating diversity and complexity. One of them is the
publicly available dataset i.e. Chars74K comprising of 7705 characters and has
upper and lowercase English alphabets, along with numerical digits. While the
other dataset created locally consists of 4320 characters. The local dataset
consists of 62 classes and was created by 40 subjects. It also consists upper
and lowercase English alphabets, along with numerical digits. The overall
dataset is divided in the ratio of 80% for training and 20% for testing phase.
The time required for training phase is approximately 90 minutes. For
validation part, the results obtained were compared with the groundtruth. The
accuracy level achieved with AlexNet was 77.77% and 88.89% with Google Net. The
higher accuracy level of GoogleNet is due to its unique combination of
inception modules, each including pooling, convolutions at various scales and
concatenation procedures.
