Recogneyes: a raspberry pi-based philippine banknote and coin value recognition system using vision-language model with audio and vibrotactile feedback for the visually impaired/ Sharia Dolce B. Barruga, Roldan Jr. C. Baruel, Federico III D. Chavez, Micko B. Fortes, Richell Mark B. Miguel, and Mercedez D. Pestelos.--

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Barruga, Sharia Dolce B [author]

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TextPublication details: Manila: Technological University of the Philippines, 2025.Description: xv, 204pages: 29cmContent type:

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BTH TK 870 B37 2025

Dissertation note: College Of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025. Summary: Visual impairment affects millions globally, with many cases remaining untreated despite advancements in modern medicine. As a result, individuals often rely on assistive technologies; however, there is a limited number of tools specifically designed for currency recognition. This study presents RecognEYES, a wearable, Raspberry Pi–based recognition system for Philippine banknotes and coins, integrating a Vision-Language Model with audio and vibrotactile feedback. It utilizes Pi Camera v2 to capture images and process it through Raspberry Pi 4B. The data is sent to a trained GPT-4o — the VLM with the highest mAP upon benchmark. Output is presented via speakers and gloves for audio and vibrotactile feedback, respectively. The system was deployed at RM Massage Clinic and evaluated by 15 visually impaired working adults. Feedback collected via a Likert scale indicated mean scores of 3.76 for usability, 3.68 for comfort, and 4.31 for marketability, suggesting high potential for real-world application. Accuracy testing, validated by a Professional Electronics Engineer (PECE), demonstrated optimal detection distances of 15–20 cm for coins with accuracies of 88.10% and 90.48%, and 10–40 cm for banknotes with 100% accuracy. For optimal performance, it is recommended that the device is used in well-lit or outdoor environments.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 870 B37 2025 (Browse shelf(Opens below))	c.1	Not for loan		BTH0006442

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Bachelor's thesis

College Of Engineering.--
Bachelor of science in electronics engineering: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

Visual impairment affects millions globally, with many cases remaining untreated
despite advancements in modern medicine. As a result, individuals often rely on assistive
technologies; however, there is a limited number of tools specifically designed for currency
recognition. This study presents RecognEYES, a wearable, Raspberry Pi–based
recognition system for Philippine banknotes and coins, integrating a Vision-Language
Model with audio and vibrotactile feedback. It utilizes Pi Camera v2 to capture images and
process it through Raspberry Pi 4B. The data is sent to a trained GPT-4o — the VLM with
the highest mAP upon benchmark. Output is presented via speakers and gloves for audio
and vibrotactile feedback, respectively. The system was deployed at RM Massage Clinic
and evaluated by 15 visually impaired working adults. Feedback collected via a Likert scale
indicated mean scores of 3.76 for usability, 3.68 for comfort, and 4.31 for marketability,
suggesting high potential for real-world application. Accuracy testing, validated by a
Professional Electronics Engineer (PECE), demonstrated optimal detection distances of
15–20 cm for coins with accuracies of 88.10% and 90.48%, and 10–40 cm for banknotes
with 100% accuracy. For optimal performance, it is recommended that the device is used
in well-lit or outdoor environments.

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