Real-time Filipino sign language to text translation using region-based convolutional neural network / (Record no. 33840)

MARC details
000 -LEADER
fixed length control field 02390nam a2200301 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20261021133020.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 261021s2024 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency CSPC
Language of cataloging eng
Transcribing agency CSPC
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSCS P223r 2024
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Parato, Lei I.,
Relator term author.
245 10 - TITLE STATEMENT
Title Real-time Filipino sign language to text translation using region-based convolutional neural network /
Statement of responsibility, etc. Lei I. Parato, Angelika V. Braga, and Jemima R. Villamer.
300 ## - PHYSICAL DESCRIPTION
Extent 180 leaves.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Theses (BSCS) - Computer Science.
Name of granting institution Camarines Sur Polytechnic Colleges, 2024.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references.
520 3# - SUMMARY, ETC.
Summary, etc. Filipino Sign Language (FSL) is the primary communication method for the Deaf com- munity in the Philippines, however limited knowledge of the language among the hearing population creates significant communication barriers. This study addresses this issue by developing a real-time FSL-to-text translation system. While deep learning has shown po- tential in gesture recognition, the lack of high-quality, large-scale FSL datasets hinders progress in this field. To bridge this gap, the study aimed to create a culturally authen- tic FSL dataset, build an accurate recognition model, and develop a system to deal with the current gaps in FSL research. A dataset of 846 video clips featuring 118 FSL ges- tures was created in collaboration with the Deaf community, ensuring linguistic accuracy. The system employed a Faster R-CNN model for gesture recognition, using a server-client architecture for real-time processing. Each frame of the video were extracted using Con- volutional Neural Networks, while Faster R-CNN and Gated Recurrent Units classified the signs. The resulting system achieved 90% accuracy with strong precision, recall, and F1-scores, demonstrating its potential to address communication challenges for the Deaf community.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Braga, Angelika V.,
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Villamer, Jemima R.,
Relator term author.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Oñate, Joseph Jessie S.,
Relator term adviser.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Prianes, Freddie B.,
Relator term chair, panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Benitez, Ian P.,
Relator term panelist..
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Onesa, Rosel O.,
Relator term panelist.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part P223r
Item part BSCS
Call number prefix UTH
Call number suffix 2024
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 10/21/2026   UTH BSCS P223r 2024 007623UM 10/21/2026 1-1 10/21/2026 Unpublished Materials