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How to render the Nepali or Hindi text as label?

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  This method allows you to plot directly using specific fonts. You can choose the font of your choice, as here the   Mangal   font is used according to your choice. All you have to do is apply the font properties to each label. # -*- coding: utf-8 -*- import matplotlib.pyplot as plt from matplotlib.pyplot import figure from matplotlib.font_manager import FontProperties font_prop = FontProperties(fname= 'Mangal.ttf' , size= 18 ) figure(num= None , figsize=( 15 , 6 ), dpi= 80 , facecolor= 'w' , edgecolor= 'k' ) a = [( 'सरकारले' , 3410 ), ( 'अर्ब' , 3143 ), ( 'आर्थिक' , 3026 ), ( 'रुपैयाँ' , 2965 ), ( 'कारण' , 2758 ), ( 'काम' , 2745 ), ( 'व्यवस्था' , 2683 ), ( 'समेत' , 2580 )] b = dict (a) ax = plt.subplot() val_val = b.values() key_val = b.keys() br = plt.bar(key_val, val_val) for label in (ax.get_xticklabels() + ax.get_yticklabels()): label.set_fontproperties(font_prop)

Image Captioning Deep Learning Model | Generate Text from Image | Introduction & Demo

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https://github.com/nitinkaushik01/Deep_and_Machine_Learning_Projects Build_ChatBot_using_Neural_Network Initial Commit 4 years ago Build_Clothing_Prediction_Flask_Web_App Initial Commit 3 years ago Build_Sentiment_Analysis_Flask_Web_App Initial commit of Image Caption Project 3 years ago Churn_Prediction_of_Customers Upload Dataset 3 years ago Convert_Color_Image_to_Pencil_Sketch Initial commit 3 years ago Customer_Lifetime_Value Initial Commit 3 years ago Customer_Segmentation_using_RFM_and_KMeans_Models Initial Commit 3 years ago Image_Caption_Project Initial commit of Image Caption Project 3 years ago Language_Translator_using_Python_and_GoogleAPI Initial Commit 3 years ago Speech_Recognition_Youtube_and_VLC_Player_project Initial commit 3 years ago Building an Image Captioner with Neural Networks # Implementation of "Show, Attend and Tell: Neural Caption Generator With Visual Attention" https://github.com/yunjey/show-attend-and-tell https://github.com/yunjey/show-attend-a

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face-recognition-attendance

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  Source Code: https://github.com/AmarShaikh/face-re... import cv2 import numpy as np import face_recognition import os from datetime import datetime import pyodbc path='trainimg' images=[] imgLabel=[] mylst=os.listdir(path) for cl in mylst:     curimg=cv2.imread(f'{path}\\{cl}')     images.append(curimg)     imgLabel.append(os.path.splitext(cl)[0]) def findEncodings(images):     encodLst=[]     for img in images:         img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)         encode=face_recognition.face_encodings(img)[0]         encodLst.append(encode)     return encodLst encodlstKnowFaces=findEncodings(images) def markAttendance2(name,inTime,InDate):     conn = pyodbc.connect('Driver={SQL Server};'                       'Server=DESKTOP-1F0T08U;'                       'Database=attendancedb;'                       'Trusted_Connection=yes;')     cursor = conn.cursor()              sql='''insert into attendancedb.dbo.tbl_attendance (N