| 000 -LEADER |
|---|
| fixed length control field |
01617nam a2200169Ia 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200308073501.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
190927s9999 xx 000 0 und d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
T Ab64 2011 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Preferred name for the person |
Ablanquee, Maria Fatima |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Offline handwritten character recognition using artificial neural network |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of Publication |
Cebu City |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of Publisher |
CIT-U |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Date of Publication |
2011 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This project is entitled OFFLINE HANDWRITTEN CHARACTER RECOGNITION USING NEURAL NETWORK. Neural Networks grew out of research in Artificial Intelligence; specifically, attempts to mimic the way human brain thinks. They process information the way the human brain does. They learn the input-output relationship through training. They cannot be programmed to perform a specific task. Neural Networks nowadays are gaining much attention since they are very useful in applications that involve solving very complex problems where the conventional algorithms will not work. <br/> In this project, a system is presented to recognize handwritten non-cursive English UPPERCASE LETTERS using Neural network. The system will accept scanned image containing handwritten non-cursive English uppercase letters which will first undergo a preprocessing stage to remove noise and will be segmented after into suspected relevant characters. The detected characters will be used for training and recognition stages wherein a learning rule used by neural network is implemented. At the end of the training, when the network has learned, the network should be able to recognize the inputs correctly or with minimal errors. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| -- |
000-099 |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Item type |
RESERVED BOOKS |
| Source of classification or shelving scheme |
|