Abstract:
The advancement of sophisticated scientific techniques in today's world is pushing
the limits of human outreach in various fields oftechnology further. One such field is
that commonly known as OCR (Optical Character Recognition) field of character
recognition.
The goal ofthis project is to develop algorithms for image recognition to identify the
handwriting on postal letters and parcels. This article explores the various techniques
used for handwriting recognition. Different stages involving the processing of
images, such as pre-processing, extraction of features and prediction will be studied
and addressed. Finally, the algorithm end product will be written in the software
named Ying.
The program is created using the Artificial Neural Network technique. The main
advantage of using this strategy is that it offers extraction and identification capabilities
which are ideal for recognition of character. VGG16, Convolutionary Neural Network
(CNN) architecture, model is addressed and Error-back propagation algorithm was used
because of its capacity to shape internal character representations in classification. A
suitable collection oftraining parameters is specified after trials and errors and a network
configuration is generated that consists of 1 input layer, 2 hidden layers and 1 output
layer with 69 input neurons, 324 neurons for both hidden layers and 38 neurons for
output layer. The system initially proceeds with threshold, inverting, and smoothing the
pre-process of the captured image. In the phase sampling resizing, and selection of
features are often done. The feed forward process is then invoked through the
network to yield an output matrix. The recognized character can be determined based
on output matrix. This system is designed to personalize an individual user's network.
The report also contains recommendations for future development and conclusions
