H2P handwriting recognition to powerpoint presentation (an OCR project) - Ang, Aguendoline C. (2001)
This document describes a handwritten character recognition system that enables to convert a bitmap image to a PowerPoint template. The input image is scanned using 300x300 dpi resolution scanner. It is imposed that the data be written in a white bond paper using a writing pen. Also, it is restricted that the data be written in print and not cursive. Characters are then extracted from the scanned image through segmentation process, which allows each character to be separated from one another. The team used heuristics to identify and eliminate noise from the image. The isolated character images are then stored in a text file, which is later used as input to the neural network for training and recognition. The segmentation part is performed by making use of the team's modified version of "labeling of connected components". The team adopted an enhanced version of Welstead's neural network from his book, "Neural Network and Fuzzy Logic" as a neural network implementation of the program. This neural network has 144 input nodes, output nodes and 40 hidden nodes.