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ARTIFICIAL INTELLIGENCE POWERED GOVERNMENT-ISSUED DOCUMENT
VERIFICATION SYSTEM IN UNIVERSIDAD DE MANILA USING RANDOM
FOREST
Ronald B. Fe nandez
ORCID: 0009-0007-0979-6315
P o esso , College o Compu ing S udies, Uni e sidad De Manila, Philippines
Ma cus Riley A. Faus ino
ORCID: 0009-0000-2910-7053
Unde g adua e S uden , College o Compu ing S udies, Uni e sidad De Manila, Philippines
Naomi T ini y A ibuabo
ORCID: 0009-0001-8921-1052
Unde g adua e S uden , College o Compu ing S udies, Uni e sidad De Manila, Philippines
Jaybe B ian D. Cas illo
ORCID: 0009-0004-7249-8642
Unde g adua e S uden , College o Compu ing S udies, Uni e sidad De Manila, Philippines
Jhenina B. Cadano
ORCID: 0009-0003-5399-4918
Unde g adua e S uden , College o Compu ing S udies, Uni e sidad De Manila, Philippines
ABSTRACT
The ising numbe o s uden en ollmen s in ins i u ions o highe lea ning has made i e en mo e di icul o
check o icial go e nmen documen s. Manual checks a e ime-consuming, p one o e o and suscep ible o
sophis ica ed o ge y solu ions, unde mining ins i u ional in eg i y and e icacy. The pape , en i led A i icial
In elligence-Powe ed Go e nmen Documen Ve i ica ion Sys em in Uni e sidad de Manila Using Random
Fo es , o e s an app oach o he p ocess o documen au hen ica ion ha is au oma ed by employing Op ical
Cha ac e Recogni ion (OCR) and machine lea ning. OCR was used o iden i y impo an cha ac e is ics in
scanned go e nmen -p o ided documen s like bi h ce i ica es, o e ce i ica es, and licenses, and was
pa icula ly conce ned wi h such issues as ex alignmen , on consis ency, p esence o wa e ma ks, and
signa u e e i ica ion. The ea u es we e hen ca ego ized wi h he help o he Random Fo es algo i hm ha has
p o en o be mo e accu a e, scalable and esis an o o e i ing. I a documen was iden i ied as a suspicious
one, i was e iewed manually, which gua an eed an ex a secu i y measu e. The Agile So wa e De elopmen
Li e Cycle had been aken up in he s udy, and, as a esul , he equi emen s ga he ing, design, de elopmen ,
es ing, and deploymen s ages we e conduc ed in an i e a i e manne . The eliabili y, usabili y, and
e ec i eness o he sys em we e es ed in e ms o unc ional, in eg a ion, pe o mance, and use accep ance.
Findings showed ha p ocessing speed, accu acy and aud de ec ion had imp o ed signi ican ly when
compa ed o adi ional manual p ac ices. In he case o he Uni e sidad de Manila, he sys em sa es he
adminis a i e load, secu i y in he au hen ici y o he s uden eco ds and p e en ion o audulen admission. In
addi ion o ins i u ional ad an ages, he s udy will add o he con inuous esea ch and echnical inno a ions in
he ield o documen e i ica ion wi h AI. All in all, he p oposed sys em o e s a scalable, cos e ec i e and
sma solu ion which ein o ces academic in eg i y, builds on ins i u ional us and ope a ional e iciency in
documen managemen .
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Keywo ds:
A i icial In elligence (AI), Documen Ve i ica ion Sys em, Random Fo es Algo i hm, Op ical Cha ac e
Recogni ion (OCR), F aud De ec ion, Machine Lea ning, Go e nmen -Issued Documen s, Au oma ion in
Ve i ica ion.
INTRODUCTION
The in eg i y o documen s has become a key issue o o ganiza ions ha ope a e wi hin he mode n digi al
en i onmen and equi e documen s ha a e au hen ic as a way o p o ec ing anspa ency, us , and in eg i y.
The apid de elopmen o echnology has eased he de elopmen and al e a ion o documen s, bu i has also
c ea ed addi ional ways o o ge y and coun e ei ing. As aud keeps on ad ancing, hey indeed p esen
signi ican h ea s o socie y, he co po a e sec o , esea ch and lea ning ins i u ions ha ely on he c edibili y o
he p esen ed documen s o accep admissions, secu e employmen , and es ablish hei iden i y. The g owing
complexi y o documen alsi ica ion equi es co espondingly sophis ica ed de ec ion and alida ion me hods,
especially by using a i icial in elligence (AI) and machine-lea ning echnologies.
Machine Lea ning, pai ed wi h A i icial In elligence and Op ical Cha ac e Recogni ion, p esen s a new
app oach o he ask o coun e ac ing documen aud by au oma ing he p ocesses o iden i ica ion and
e i ica ion. OCR enables he segmen ing and coding o ex and images, and he machine-lea ning algo i hms,
namely he Random Fo es , ains on he pa e ns ha a e p esen in legi ima e and o ged documen s and
p oduces accu a e classi ica ions. Random Fo es algo i hm is a model o ensemble (many decision- ees),
which enhances e i ica ion, analyzing such ea u es like on ype, ex alignmen , wa e ma k p esence, and
signa u es. This mul i-laye ed me hod addi ionally imp o es he de ec ion accu acy, as well as educes he
possibili y o human e o , p oducing a quicke and mo e eliable e i ica ion p ocedu e.
The cu en esea ch aims o build an AI-based documen e i ica ion sys em in he case o he Uni e sidad de
Manila by using he andom o es algo i hm o au oma e and supplemen he e i ica ion o academic and
ins i u ional documen s. The sys em will be designed o wo k h ough a secu e local se e , wi h an in e ace
based on Flu e o enable accessibili y ac oss pla o m. The sys em will ha e he po en ial o classi y uploaded
documen s e icien ly using OCR, AI, and Machine Lea ning o conside hem ei he as alid o suspicious iles
and ma k he suspicious iles ha need alidi y e i ica ion. Wi h his inno a ion, he uni e si y will be able o
simpli y he adminis a i e ope a ions, minimize he isk o audulen submissions, and become o e all mo e
secu e as an ins i u ion, hus adding o a mo e e icien and mo e echnology-ad anced academic se ing.
OBJECTIVES
The main goal o he esea ch is o plan and design an AI-based documen e i ying sys em, which uses he
Random Fo es machine lea ning algo i hm, o imp o e he de ec ion and check o o ged s uden ’s go e nmen -
issued documen s a he Uni e sidad de Manila. This is o enhance he accu acy o academic eco ds and o icial
uni e si y p ocesses, eliabili y as well as he secu i y o s uden ’s o icial eco ds.
Also, he objec i es a e he ollowing:
1) To de elop and es ablish an AI-based e i ica ion sys em ha would be able o analyze and con i m he
au hen ici y o go e nmen documen s o e ed by he s uden s.
2) To use he Random Fo es algo i hm in classi ying and de ec ing audulen o manipula ed documen s
using lea ned aining samples o genuine and ake samples.
3) To inco po a e he de eloped sys em as a pa o he cu en p ocess o managing documen s in he
Uni e sidad de Manila o au oma ize and simpli y he e i ica ion p ocess.
4) To compa e he sys em pe o mance wi h ela ion o accu acy, eliabili y, p ocessing ime and use
sa is ac ion wi h he adi ional manual e i ica ion echniques.
5) To ha e a sa e and scalable s uc u e ha would allow he u he de elopmen o he documen
e i ica ion p ocess in o he depa men s o o ganiza ions.
Rela ed S udies
This s udy by [1] Su mieda (2022) de eloped an OCR-enhanced digi al asse managemen sys em aimed a
digi izing and o ganizing school documen s. The sys em imp o ed accessibili y and minimized manual
encoding e o s, emphasizing how OCR echnology can s eamline documen p ocessing wi hin academic
ins i u ions. This esea ch demons a es he e iciency o OCR in managing educa ional eco ds and suppo s he
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po en ial o AI-assis ed documen e i ica ion in uni e si ies.
This wo k by [2] Buc uanon e al. (2021) in oduced “Docudile,” a hyb id ule-based and OCR-suppo ed
documen s uc u e classi ica ion sys em designed o academic use. The sys em e ec i ely ca ego ized
documen layou s and o ma s, hough i encoun e ed limi a ions in handling i egula ly o ma ed iles. The
wo k highligh s he alue o combining OCR and machine lea ning o documen analysis and classi ica ion,
con ibu ing o mo e eliable academic eco d managemen .
This s udy by [3] Pino e al. (2022) ocused on de eloping a block-le el OCR sys em using Suppo Vec o
Machine (SVM) o ansli e a e Baybayin ex s in o he La in alphabe . By in eg a ing OCR wi h machine
lea ning, he sys em achie ed high ecogni ion accu acy, showing he easibili y o AI-d i en models in
p ocessing complex documen sc ip s. This con ibu es o he ounda ion o documen e i ica ion echnologies
ha ely on ea u e ecogni ion and pa e n consis ency.
This s udy by [4] Dis o e al. (2021) examined he accep ance and use o A i icial In elligence in local
go e nance wi hin he Municipali y o Ca mona, Ca i e. Using amewo ks om he Technology Accep ance
Model and UTAUT, he esea che s ound ha local o icials displayed posi i e a i udes and beha io al
in en ions owa d AI adop ion. Thei indings emphasize how AI can enhance e iciency and da a secu i y in
public ope a ions, pa alleling i s po en ial applica ions in academic documen managemen .
This s udy by [5] Rane a (2024) p esen ed a compa a i e analysis o na u al language p ocessing (NLP)
embedding echniques on Philippine Sup eme Cou case decisions. The esea che compa ed embedding
models like Doc2Vec and TF-IDF o imp o e documen e ie al and classi ica ion accu acy. This esea ch
demons a es how machine lea ning and ex analy ics can enhance documen -based sys ems, se ing as a local
p eceden o AI-d i en e i ica ion models in academic ins i u ions.
This s udy by [6] Seu e e al. (2020) p oposed a o ensic documen examina ion sys em ha used ensemble
lea ning echniques, including Random Fo es , o de ec o ged documen s. Thei indings showed ha ensemble
me hods e ec i ely iden i ied a ia ions in ink ex u e, ex s uc u e, and on a angemen s. This s udy
ein o ces he Random Fo es algo i hm’s eliabili y in dis inguishing au hen ic om coun e ei documen s,
aligning closely wi h he ocus o his esea ch.
This s udy by [7] Pa il and Maheshwa i (2021) compa ed machine lea ning algo i hms—Random Fo es ,
Suppo Vec o Machine, and K-Nea es Neighbo s— o online signa u e e i ica ion. Resul s indica ed ha
Random Fo es achie ed highe p ecision and consis ency, pa icula ly in de ec ing o ge ies unde a ied
handw i ing pa e ns. This emphasizes Random Fo es ’s s eng h in au hen ica ion asks, alida ing i s use in
documen e i ica ion sys ems.
This s udy by [8] Wang e al. (2022) applied deep lea ning-based OCR o ex ac and e i y ex om complex
egula o y documen s. By combining con olu ional neu al ne wo ks (CNN) wi h OCR, he model achie ed high
p ecision e en wi h inconsis en layou s and image noise. This demons a es he scalabili y o combining OCR
and AI o ad anced documen ecogni ion sys ems.
This s udy by [9] Ku i e al. (2025) p esen ed a legal documen au hen ica ion sys em in eg a ing OCR, Na u al
Language P ocessing, and Con olu ional Neu al Ne wo ks. The sys em examined bo h ex ual and isual
documen ea u es o con i m au hen ici y, achie ing high de ec ion accu acy o alsi ied pape s. Thei esea ch
highligh s he po en ial o combining mul iple AI echniques o obus e i ica ion, closely pa alleling he aim
o he cu en s udy.
This s udy by [10] Lee e al. (2023) explo ed o ensic documen pape iden i ica ion using hyb id ea u e
ex ac ion and machine lea ning classi ica ion. The esea che s achie ed high accu acy by combining ex u e
and pa e n-based ea u es wi h AI classi ie s, enabling p ecise di e en ia ion be ween genuine and coun e ei
documen s. This wo k emphasizes he e ec i eness o machine lea ning in o ensic documen au hen ica ion,
ein o cing i s ele ance o he p esen s udy.
METHODOLOGY
The esea ch design used in his pape was a de elopmen al esea ch design whe e a Documen Ve i ica ion
Sys em wi h AI is de eloped o e i y go e nmen -issued documen s in academic use a he Uni e sidad de
Manila. The sys em is de eloped wi h he help o machine lea ning, in pa icula , he Random Fo es algo i hm,
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o classi y and check documen s wi h he ea u es iden i ied wi h he help o Op ical Cha ac e Recogni ion
(OCR) and image analysis. OCR echnology was used o ex ac ex ual and isual ea u es like on s yle, ex
layou , wa e ma k, and signa u es ha we e hen p ocessed by he Random Fo es model o iden i y au hen ici y
o a documen . This app oach has p o ided a dependable, speedy and au oma ed e i ica ion sys em ha
mi iga es human e o , manual o e load and s eng hens ins i u ional secu i y agains audulen documen s.
Sys em De elopmen Model
Figu e 1: Agile SDLC o Documen Ve i ica ion Sys em
The p oponen s chose he Agile Me hodology because o i s i e a i e cycles and adap abili y o use
eedback, making i mo e con enien o a machine lea ning sys em since i has a dynamic p ocess o
de elopmen . This me hod o app oach aligned wi h he con inuous imp o emen o he iden i ica ion sys em.
1) Planning
Talk o he uni e si y egis a and o school acul y on hei documen e i ica ion challenges, he
equi ed ea u es o he documen e i ica ion, o ins ance, use au hen ica ion, documen upload
me hods, and e i ica ion p ocesses, e c. de elopmen
2) Design
The ex ac ion o he cha ac e s om he image by Op ical Cha ac e Recogni ion (OCR) and hen use
o he Random Fo es algo i hm o e i y documen s. The design should be also easy o use and should
comply wi h he use equi emen s.
3) De elopmen
The de elopmen and implemen a ion o he use in e aces, backend s uc u e, and in eg a ion o he
a i icial in elligence model we e ca ied ou h ough i e a i e de elopmen cycles. This app oach
allowed he sys em’s ea u es o be buil , es ed, and e ined in inc emen al s ages, enabling con inuous
imp o emen and imely modi ica ion based on pe o mance and unc ionali y ou comes.
4) Tes ing
The con inuous es ing while de eloping he sys em and i in ol ed uni es ing, in eg a ion es ing and
use accep ance es ing. These gua an eed he smoo h unc ionali y be ween he di e en elemen s, he
unc ionali y was sa is ac o y o he use and ha he en i e sys em p oduced co ec and consis en
e i ica ion ou comes.
5) Deploymen
Once unc ional, he sys em was deployed o use applica ion. The implemen a ion enabled he
uni e si y egis a and acul y o emba k on he u iliza ion o he documen e i ica ion ea u es in
p ac ice. New upda es and enhancemen s we e eleased a he end o e e y sp in and p o ided use s
wi h inalized unc ionali ies and imp o ed pe o mance wi h ime.
6) E alua ion and Re inemen
A e deploymen , he esul s o e e y sp in we e discussed wi h he s akeholde s. Facul y and
egis a eedback we e made, and he ea u es we e op imized o enhance he usabili y and s eng hen
accu acy. The lessons ha we e acqui ing du ing each o hese cycles we e used o make he
adjus men s owa d he u he imp o emen s and he sys em became mo e e icien and esponsi e o
he needs o he ins i u ion.
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Da a Ga he ing Techniques
P ima y Da a Collec ion
1. Face o Face In e iews: Conduc ed wi h he uni e si y’s egis a and school acul y o assess documen
e i ica ion and au hen ica ion challenges h ough ace- o- ace and online mode
2. Online Su ey: Conduc ed wi h he uni e si y’s egis a and school acul y o assess documen
e i ica ion and au hen ica ion challenges h ough emails, messages, and online social media.
3. Feedback: The eedback ob ained om hese expe s se es as an impo an basis o imp o ing he
accu acy and e ec i eness o he p oposed sys em.
Seconda y Da a Collec ion
1. Re iew o Rela ed Li e a u e/S udies: An ex ensi e analysis o academic s udies, jou nal a icles, and
esea ch pape s on he AI Powe ed Documen Ve i ica ion Sys em was conduc ed. This e iew p o ided
insigh s o he p oponen s in o he challenges based on he li e a u e and s udies.
2. Academic Resea ch: Usage o online esea ch (e.g. Google Schola ) o assess help ul s udies, a icles,
esea ch, jou nals, e c. co ela ing o he s udy.
Concep ual F amewo k
The Inpu , P ocess, Ou pu Sys em A chi ec u al model p o ides a clea and concise way o desc ibe
and ep esen a sys em's unc ionali y. The diag am o e s a pe spec i e o how da a lows h ough he sys em,
ans o ming inpu s in o desi ed ou pu s. The simplici y o he diag am makes i simple o unde s and ac oss
echnical and non- echnical audiences.
Figu e 2: Concep ual F amewo k
Figu e 2 se s ou he ela ions be ween sys em en i ies ha enable accu a e, e icien , and secu e e i ica ion o
s uden documen s in Uni e sidad de Manila. Use inpu , Op ical Cha ac e Recogni ion, and he Random Fo es
algo i hm in e ac wi hin he s udy o p o ide eliable au hen ica ion esul s and con idence sco es.
Inpu Phase – The inpu phase begins when use s log in and upload scanned copies o hei go e nmen -issued
documen s, such as bi h ce i ica es, o e ’s ce i ica es, and licenses. These se e as he p ima y da a ha he
s udy will analyze o au hen ici y.
P ocess Phase – In he p ocess phase, Op ical Cha ac e Recogni ion ex ac s key documen ea u es such as ex
alignmen , on s yle, wa e ma ks, and signa u es. The Random Fo es algo i hm hen analyzes hese ea u es o
classi y he documen as au hen ic o suspicious. This ensu es a sys ema ic and au oma ed e i ica ion
p ocedu e.
Ou pu Phase – The ou pu phase p esen s he esul s o he e i ica ion, showing whe he he documen is
au hen ic o suspicious. A con idence sco e is p o ided o accu acy, and use s a e also gi en he op ion o e-
upload documen s i addi ional e i ica ion is needed.
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P o o ype
Figu e 3: Login & Signup Sc een
The sys em s a s wi h he Log In & Sign-Up sc een, whe e use s ei he en e hei c eden ials o access hei
accoun s o egis e as new use s. This s ep ensu es ha only au ho ized indi iduals can p oceed wi h he
e i ica ion p ocess. Upon success ul au hen ica ion, use s a e edi ec ed o he homepage.
Figu e 4: Home Sc een
The Homepage se es as he main dashboa d whe e use s can s a he documen e i ica ion p ocess. They a e
gi en op ions o ei he upload a documen om hei de ice’s s o age o ake a new pho o using hei came a.
This in e ace is designed o ease o na iga ion, allowing use s o mo e o wa d quickly.
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Figu e 5: Galle y Selec ion and Came a
In he Galle y Selec ion and Came a ame, use s b owse hei pho o galle y and selec a documen o cap u es
documen using hei came a in which hey wish o e i y. This s ep ensu es ha use s can con enien ly upload
p e iously sa ed images ins ead o aking a new one. Once a documen is selec ed, hey p oceed o he upload
sc een.
.
Figu e 6: Side Ba Ligh Mode
A e p ocessing, use s a e di ec ed o he Resul Sc een, whe e hey ecei e he inal e i ica ion ou come. The
sys em displays a eedback (“Documen is Au hen ic”), indica ing he accu acy o he au hen ica ion p ocess. I
he documen is success ully e i ied, use s may choose o download a e i ica ion epo . I e i ica ion ails o
he documen is lagged as suspicious, use s may be gi en op ions o ea emp e i ica ion o con ac suppo
o u he assis ance.
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RESULTS AND DISCUSSION
This sec ion summa izes and discusses he e alua ion esul s ob ained om he ISO/IEC 25010-based
ques ionnai e dis ibu ed o chosen esponden s. The s udy ocused on he AI Powe ed Documen Ve i ica ion
Sys em’s majo quali y ea u es, such as unc ional sui abili y, pe o mance e iciency, compa ibili y, usabili y,
eliabili y, secu i y, main ainabili y, and po abili y. Each sub-c i e ion was sco ed on a i e-poin Like scale,
allowing use s o iden i y bo h s eng hs and weaknesses based on hei pe cep ions and in e ac ions wi h he
sys em.
The ecei ed da a we e p ocessed ollowing he o mula o desc ip i e s a is ics, namely, means, and hen
p esen ed in he o m o g aphical ep esen a ions o cla i y he pe o mance o he sys em in ela ion o he
esea ch goals. The su ey was conduc ed on 70 esponden s who we e selec ed pu posely o p o ide signi ican
eedback ha cons i u ed an empi ical base on he e alua ion o he sys em unc ionali y, usabili y, and o e all
e ec i eness.
Figu e 7: Ba G aph o Func ional Sui abili y
The sys em sco ed an a e age mean o 4.78 (Excellen ) on he Func ional Sui abili y. The answe o his
ques ion was s ongly a i ma i e wi h he esponden s o he opinion ha he sys em is doing i s job accu a ely,
au oma ing he e i ica ion o o icial documen s like bi h ce i ica es, o e IDs and licenses. They emphasized
ha he sys em is e icien in iden i ying majo ea u es such as ex alignmen , on consis ency, wa e ma ks,
and digi al signa u es wi h he help o Op ical Cha ac e Recogni ion (OCR) ha is capable o gi ing accu a e
documen au hen ici y esul s.
Figu e 8: Ba G aph o Pe o mance E iciency
The sys em ecei ed a Gene al Weigh ed Mean o 4.40 (Abo e A e age) in Pe o mance E iciency. The
esponden s also indica ed ha he sys em is as in p ocessing and classi ies scanned documen s wi hou any
hesi a ion o c ashing o he sys em and based on he Random Fo es algo i hm. I did no lose i s s abili y in
pe o mance and eal- ime esponse e en when dealing wi h se e al e i ica ion eques s.
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h ps://ije m.com/
IJETRM (h p://ije m.com/) [149]
Figu e 9: Ba G aph o Compa ibili y
Compa ibili y ound a Gene al Weigh ed Mean o 4.12(Abo e A e age). The esponden s a i med ha he
applica ion wo ks eliably ac oss he desk op and mobile pla o ms and in eg a es well wi h scanning and
documen inpu de ices. Va ious documen ypes (PDF, JPG, PNG) can be p ocessed by he sys em wi hou
compa ibili y p oblems.
Figu e 10: Ba G aph o Usabili y
Usabili y had an a e age ma k o 4.50 (Excellen ) agains he sys em. The esponden s we e able o ag ee ha
he in e ace is easy o na iga e, in ui i e and isually s uc u ed. The e i ica ion p ocess comes wi h less use
aining and he layou o he e i ica ion is use iendly as he e i ica ion esul s a e clea ly shown once he
use s scan, p ocess and iew he e i ica ion esul s.
Figu e 11: Ba G aph o Reliabili y
