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01940nam a22002297a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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CITU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20211123164453.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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210913b ||||| |||| 00| 0 eng d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
621.39/9 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Suson, Jennelyn C. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
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| Title |
Non-cursive handwritten word recognizer using neural network / |
| Statement of responsibility, etc |
Jennelyn C. Suson. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
viii, 135 leaves : |
| Dimensions |
28 cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| Content type code |
txt |
| 337 ## - MEDIA TYPE |
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rdamedia |
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unmediated |
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n |
| 338 ## - CARRIER TYPE |
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rdacarrier |
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volume |
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nc |
| 500 ## - GENERAL NOTE |
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| General note |
Computer print-out |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master in Computer Science) -- Cebu Institute of Technology - University, March 2011. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Handwriting recognition is a challenging task for many real-world applications such as document authentication, form processing, postal address recognition, bank check recognition, and interpretation of historical documents. With this, researchers have put an enormous effort into developing various techniques for handwriting recognition. This study presents a non-cursive handwritten word recognition system using neural network. This system has the ability to receive handwritten word from an input device. This handwritten word is preprocessed, cropped and segmented, and then feed into the neural network of training, classification and recognition. Handwritten word capturing of the system is done using a mouse-an input device, and is sensed online, that is, automatic conversion of word as it is written. Kohonen Self-Organizing Map (Kohonen SOM) neural network and image processing are the two main techniques applied. This study ensures that the processed word image data is read exactly the same with the original non-cursive handwritten word. The results show a practical application of the Kohonen neural network and are to what uses this technology might have. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Optical character recognition devices. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Writing |
| General subdivision |
Data processing. |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
THESIS / DISSERTATION |