Published on Feb 21, 2020
The Project is an optical character recognition application using artificial neural networks. The central objective of this project is demonstrating the capabilities of Artificial Neural Network implementations in recognizing extended sets of optical language symbols. The applications of this technique range from document digitizing and preservation to handwritten text recognition in handheld devices.
The main purpose of the Unicode Optical Character Recognition using Artificial Neural Netwirks is to avoid the classic difficulty of being able to correctly recognize even typed optical language symbols is the complex irregularity among pictorial representations of the same character due to variations in fonts, styles and size. This irregularity undoubtedly widens when one deals with handwritten characters.
The conventional programming methods of mapping symbol images into matrices, analyzing pixel and/or vector data and trying to decide which symbol corresponds to which character would yield little or no realistic results. Clearly the needed methodology will be one that can detect 'proximity' of graphic representations to known symbols and make decisions based on this proximity.
To implement such proximity algorithms in the conventional programming one needs to write endless code, one for each type of possible irregularity or deviation from the assumed output either in terms of pixel or vector parameters, clearly not a realistic fare
Hardware Requirements :
Processor Pentium-III Intel 80486
Clock speed 800 MHZ or above
RAM 512 MB Ram or above
Monitor 15'' Color
HDD 20 GB
FDD 1.44 MB
CD drive LG 52X
Keyboard Standard 102 keys
Mouse 3 buttons
Software Requirements :
Servers - Microsoft Windows 2000, Microsoft SQL Server/ORACLE
Clients - Microsoft Internet Explorer and also FireFox, NetScape etc
Tools - Microsoft Visual Studio.NET
Services - ASP.NET XML Web Services