MARC details
| 000 -LEADER |
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03457ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111025.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250206t20242024my a|||f |||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010002 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .A45 2024 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ahmed Ali Mohammed Al-saffar, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
A sequential handwriting recognition model based on a dynamically configurable convolution recurrent neural network and hybrid salp swarm algorithm / |
| Statement of responsibility, etc. |
Ahmed Ali Mohammed Al-saffar, |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 142 pages : |
| Other physical details |
illustration ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
The processing of automatically generated images of documents, which is a complex and expensive but necessary component of handwriting recognition, and which has always drawn the attention of engineers and scientists. The definition includes processing of an image in which handwritten letters or numbers exist with the term handwriting recognition. The practical applications of handwriting recognition in many of the current real-world applications rely on the processing of sequential texts, where the language must be detected with efficiency, suggesting the development of a flexible handwriting recognition model. The purpose of this research is to finally describe an automated and flexible way of finding the most appropriate CRNN (Convolutional Recurrent Neural Network) architecture for predictive-sequence related tasks. This research present a dynamic configurator of the CRNN (DC-CRNN), geared for sequence learning in the context of handwriting recognition, inspired by bio-inspired approaches. The built DCCRNN is based on the Salp Swarm optimization Algorithm (SSA), a processor that given a particular dataset will find the best CRNN’s structure and hyperparameters. The present research offers a unique hybridization of SSA with the Late Acceptance Hill-Climbing (LAHC) to further strengthen the optimization process. Experiments were performed on two well-known datasets of Arabic and English handwriting, IAM and IFN/ENIT. The empirical results display that the proposed DC-CRNN and its SSA hybridization are capable of autonomously finding and selecting the best CRNN for a specific dataset. The experimental results indicate that the implementation outperformed other classic handcrafted CRNNs in terms of performance, accuracy, and overall quality for predictive sequential handwriting recognition tasks. Additionally, the SSA is greatly improved during the search process when combined with LAHC, resulting in further improved performances. The given research contributes significantly to the field, as it provides a novel way of finding the best CRNNs for predictions of sequence in handwriting recognition that can be replicated in future and various language and script. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |