Final report artificial intelligence theme vietnam currency identification

At the same time, the development of systems to identify and classifies Vietnam''''s currency important requirement in financial, business and commercial activities.In this topic, we will l

BỘ GIÁO DỤC VÀ ĐÀO TẠO

TRƯỜNG ĐẠI HỌC SƯ PHẠM KỸ THUẬT TP.HCM KHOA:ĐÀO TẠO CHẤT LƯỢNG CAO

-FINAL REPORT ARTIFICIAL INTELLIGENCE

THEME:

VIETNAM CURRENCY IDENTIFICATION

GVHD: Associate Professor Dr Nguyễn Trường Thịnh NAME: Nguyễn Ngọc Phúc

MSSV: 20146269

HCM, Ngày 22 Tháng 5 Năm 2023

In the era of technology 4.0, artificial intelligence is becoming a hot research field and has great potential for development At the same time, the development of systems to identify and classifies Vietnam's currency important requirement in financial, business and commercial activities

In this topic, we will learn about the identification of the Vietnamese currency, the methods and techniques used to solve this problem, as well as its application in practice Thereby, we will see the importance and potential of artificial intelligence in improving people's lives

I Introduction

The topic is "Identifying Vietnamese currency" is a topic in the field of artificial

intelligence, studying the ability of computers in Vietnamese image processing to generate input data

The objective of the project is to learn and analyze techniques and methods to recognize Vietnamese banknotes (from VND 5,000 to VND 500,000) by artificial intelligence and determine its application in practice

The scope of the topic covers methods and techniques of emotion recognition such

as neural networks, deep learning, facial recognition techniques, voice, natural language processing, and its applications in various fields such as advertising markets, education, healthcare, and entertainment

The significance of the topic is to make computer systems smarter and more sophisticated in interacting and communicating with humans This will make it possible to shorten the process of classifying money and develop methods to detect counterfeit money, thereby bringing higher economic and social efficiency

II Method

The image-based method of identifying banknotes is one of the common methods used for currency identification This method uses software and computer technology to analyze features in images, such as colors and images in banknotes,

to determine human emotions

Image-based currency identification can be used in economic sectors such as vending machines, especially after the recent COVID-19 outbreak, contactless direct pay has also been developed

However, image-based currency identification still faces challenges One of the challenges are the accuracy of recognizing bills of the same color that are still hitting In addition, the processing and analysis of different data between banknotes also require advanced techniques and technologies to achieve high accuracy

III Implement

The training model will be implemented on the colab:

a) Data preparation

The dataset contains images with labels as monetary values that are collected and processed to match the training process This dataset is divided into a training set and a test set to evaluate the accuracy of the model

b) Model building

# connect to Drive to get data

from google.colab import drive

drive.mount('/content/drive')

# tải các thư viện cần thiết

import numpy as np

from os import listdir

from numpy import asarray, save

from keras.utils import load_img, img_to_array, to_categorical

from keras.models import Sequential, load_model

from keras.layers import

Dense,Dropout,Flatten,Conv2D,MaxPooling2D,Normalization,LeakyReLU from keras.optimizers import Adam

from keras.losses import categorical_crossentropy

import matplotlib.pyplot as plt

from keras.utils.image_utils import img_to_array

#lưu forder chứa dữ liệu vào 1 biếng

folder = '/content/drive/MyDrive/money/'

#tiến hành tiền xử lý dữ liệu

photos, labels = list(), list()

for file in listdir(folder):

output = 0.0

if file.startswith('5k'):

output = 1.0

if file.startswith('10k'):

output = 2.0

if file.startswith('20k'):

output = 3.0

if file.startswith('50k'):

output = 4.0

if file.startswith('100k'):

output = 5.0

if file.startswith('200k'):

output = 6.0

if file.startswith('500k'):

output = 7.0

img =load_img(folder+file,target_size=(100,200)) photo = img_to_array(img)

photos.append(photo)

labels.append(output)

photos = asarray(photos)

labels = asarray(labels)

# lưu dữ liệu vừa xử lý

save('luuhinhtien.npy',photos)

save('luutentien.npy',labels)

c) Model training

#32 lần tích chặp

model = Sequential()

model.add(Conv2D(32,kernel_size = (3,3),activation = 'relu',input_shape=(100,200,3),padding='Same'))

model.add(MaxPooling2D((2,2),padding='same'))

model.add(Dropout(0.25))

#64 lần tích chặp

model.add(Conv2D(64,(3,3),activation ='relu',padding ='same')) model.add(MaxPooling2D((2,2),padding='same'))

model.add(Dropout(0.25))

#128 lần tích chặp

model.add(Conv2D(128,(3,3),activation ='relu',padding ='same'))

model.add(MaxPooling2D((2,2),padding='same'))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(64,activation = 'relu'))

model.add(Dropout(0.25))

model.add(Dense(10,activation='softmax'))

model.compile(loss = categorical_crossentropy,optimizer = Adam(),metrics=['accuracy']) model.summary()

# gọi các dử liệu đã sử lý và biến đổi cho phù hợp với mô hình đã chuẩn bị

x_train = np.load('/content/luuhinhtien.npy')

y_train = np.load('/content/luutentien.npy')

x_train = x_train.astype('float32')/255

y_train = to_categorical(y_train,10)

#Train_model

train = model.fit(x_train,y_train,batch_size=32,epochs = 200,verbose = 1)\

After training the model, we use the command to save and download the h5 file to the machine: model.save('doancuoiky.h5')

d) Check

The accuracy is still not high, this may be due to the small number of training samples or the sharpness of the image needs to be higher

IV Interface design

The interface design process is performed on Visual Studio Code

#tải các thư viện cần thiết

import numpy as np

import matplotlib.pyplot as plt

from keras.models import load_model

from keras.utils import load_img, img_to_array

from keras.utils.image_utils import img_to_array

from tkinter import *

from tkinter.filedialog import *

from PIL import Image, ImageTk

#lưu model đã được huấn luyện trước

doan=load_model(‘doancuoiky.h5')

#chuẩn bị 1 biến dùng để xuất ảnh ra màng hình

img =0

# 1 hàm dùng để đoán giá trị tờ tiền và đưa phán đoán lên màng hình

def phandoan():

global doan

global img

#lb1.config('')

vat = {1: '5000',2:'10000', 3:'20000', 4:'50000', 5:'100000', 6:'200000', 7:'500000' } result = np.argmax(doan.predict(img),axis=1)

lb1.config(text=' {} VND'.format(vat[result[0]]))

#1 hàm dùng để tiềm ảnh và xuất lên màng hình

def doi():

global img

try:

filename=askopenfilename(initialdir='c:\\python31\\',filetypes=[('jpg files', '.jpg')]) load = Image.open(filename)

load=load.resize((200,100))

render = ImageTk.PhotoImage(load)

img = load_img(filename,target_size=(100,200))

plt.imshow(img)

img = img_to_array(img)

img=img.reshape(1,100,200,3)

img = img.astype('float32')

img =img/255

hinh.configure(image=render)

hinh.image = render

DOAN ["state"] = "normal"

lb1.config(text='')

except:pass

#1 hàm dùng để xóa các ảnh và giá trị đã đoán

def xoa():

global hinh

hinh.destroy()

hinh= Label()

hinh.place(x=50, y=215)

DOAN ["state"] = DISABLED

lb1.config(text='')

#tạo giao diện

root = Tk()

root.geometry("1000x600")

#lb1 để hiện giá trịu phán đoán

lb1 = Label(text='',font=('times',30))

lb2 = Label(text='Kết quả',font=('times',30))

#label dùng để hiển thị hình ảnh đã chọn

hinh= Label()

lb3 = Label(text='Ảnh Cần Xác Định',font=('times',24))

lb4 = Label(text='=>',font=('times',50))

#gắn tất cả lên giao diện

lb4.place(x=425, y=250)

lb3.place(x=50, y=50)

hinh.place(x=50, y=215)

lb1.place(x=600, y=250)

lb2.place(x=650, y=50)

#nút xóa

xoa = Button(text='Xóa/Hủy', font=('Verdana', 24),command=xoa)

xoa.place(x=400, y=450)

#nút tìm và hiển thị hình ảnh

chuyen = Button(text='Chọn ảnh', font=('Verdana', 24),command=doi) chuyen.place(x=50, y=450)

#Nút kích hoạt phán đoán

DOAN = Button(text='Định Dạng', font=('Verdana', 24),command= phandoan) DOAN.place(x=700, y=450)

#chỉ khi đã chọn hình mới được nhấn nút DOAN ["state"] = DISABLED mainloop()

V Result

VI Conclude

Visual currency recognition is an important area of research in artificial intelligence By researching and applying methods to identify features in images, we can help computers will be able to convert image data into digital data and detect features through images

In this article, we have been introduced to the basic concepts of currency identification,

as well as the applications of currency identification in life and an industry In particular,

we have learned about how to use CNN algorithms to train image recognition models Although the method of visual currency identification has made much progress, however, there are many challenges that need to be addressed These challenges include the accuracy of the system, especially in classifying banknotes of the same color, and the ability to handle low-quality images

In short, visual currency identification is very promising and highly applicable field in life and an industry Currency identification methods are currently being continuously researched and improved to achieve greater accuracy and efficiency

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