Предсказать извлеченные данные из изображения,

Question

    import nltk
nltk.download('stopwords')
import pandas as pd
import numpy as np
df = pd.read_csv("All.csv")
df
get_features = df.iloc[0:2]

get_features

df = df[pd.notnull(df['Bank Detail'])]
df.info
col = ['Bank Detail', 'Classes']
df = df[col]
df['classes'] = df['Bank Detail'].factorize()[0]
df
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import pandas as pd
from sklearn.metrics import accuracy_score
import pickle
from sklearn.model_selection import train_test_split
import cv2
import pytesseract
from pytesseract import image_to_string


stopWords = set(nltk.corpus.stopwords.words('english'))

vect = TfidfVectorizer(sublinear_tf=True, encoding='utf-8',
                             decode_error='ignore',stop_words=stopWords)
X_train, X_test, y_train, y_test = train_test_split(df["Bank Detail"], df["Classes"], 
test_size=0.33, random_state=42)

xTrain = X_train
yTrain = y_train

tfidf = vect.fit(xTrain.values.astype('U'))
xTrainvect = vect.fit_transform(xTrain)
yTrainvect = yTrain

xTestvect = vect.transform(X_test)
yTestvect = y_test

model = MultinomialNB(alpha=0.01, fit_prior=True)
model.fit(xTrainvect, yTrainvect)

ypred = model.predict(xTestvect)
score = accuracy_score(yTestvect, ypred)
print ("Accuracy: ",score)

test = "DEBIT CARD PURCHASE AT BUFFALO WILD WINGS, FARMINGTON HI, MI ON 061919 . "
new_pred = model.predict(vect.transform([test]))
print(new_pred)


img = cv2.imread("chase_bank.jpg")
get_text = pytesseract.image_to_string(img)

a_list = nltk.tokenize.word_tokenize(get_text)
print(a_list)

text_length = len(get_text)
print(text_length)

dates = []
#getting dates
for s in a_list:
  if(model.predict(vect.transform([s])) == 'Date'):
  #print(s)
  dates.append(s)

печать (даты)

Я работаю над банковскими выписками. Моя задача состоит в том, чтобы извлечь данные, суммы и описание из трех столбцов и вставить их в таблицы Excel. Как я могу найти даты, описание и суммы из извлеченного текста из изображения, чтобы я мог добавить к списку? Если кто-нибудь знает лучший подход, который я хотел бы использовать. Спасибо.

Answer 1

import nltk
nltk.download('punkt')

pip install ocrspace

import nltk
nltk.download('stopwords')

import pandas as pd
import numpy as np

df = pd.read_csv("All.csv")
df

get_features = df.iloc[0:2]

get_features

df = df[pd.notnull(df['Bank Detail'])]

df.info

col = ['Bank Detail', 'Classes']
df = df[col]

df['classes'] = df['Bank Detail'].factorize()[0]
df

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import pandas as pd
from sklearn.metrics import accuracy_score
import pickle
from sklearn.model_selection import train_test_split
import cv2
import pytesseract
from pytesseract import image_to_string

stopWords = set(nltk.corpus.stopwords.words('english'))

vect = TfidfVectorizer(sublinear_tf=True, encoding='utf-8',
                             decode_error='ignore',stop_words=stopWords)

X_train, X_test, y_train, y_test = train_test_split(df["Bank Detail"], 
df["Classes"], test_size=0.33, random_state=42)

xTrain = X_train
yTrain = y_train

tfidf = vect.fit(xTrain.values.astype('U'))
xTrainvect = vect.fit_transform(xTrain)
yTrainvect = yTrain

xTestvect = vect.transform(X_test)
yTestvect = y_test


model = MultinomialNB(alpha=0.01, fit_prior=True)
model.fit(xTrainvect, yTrainvect)

ypred = model.predict(xTestvect)
score = accuracy_score(yTestvect, ypred)
print ("Accuracy: ",score)

def test_predict():
 test = "DEBIT CARD PURCHASE AT BUFFALO WILD WINGS, FARMINGTON HI, MI ON 
   061919 . "
  new_pred = model.predict(vect.transform([test]))
  print(new_pred)
test_predict()
img = cv2.imread("bank_sheet.jpg")

#extracing text data from the image
def extracting_text_from_image():
  import ocrspace
  api = ocrspace.API()
  api = ocrspace.API('6f80b6ff6288957', ocrspace.Language.English)
  api.ocr_file('bank_sheet.jpg')
  banktext = api.ocr_file('bank_sheet.jpg')
  print(banktext)


  extracting_text_from_image()

  #get_text = pytesseract.image_to_string(banktest)
  dates = []
  amounts = []
  description = []


  # tokenizing the string and saperately getting dates, description and amount

def getting_dates_description_amount_separately():
     a_list = nltk.tokenize.line_tokenize(banktest)
    for s in a_list:
     if(model.predict(vect.transform([s])) == 'Date'):
         #print(s)
          dates.append(s)

     if(model.predict(vect.transform([s])) == 'Amount'):
        amounts.append(s)

     if(model.predict(vect.transform([s])) == 'Description'):
          description.append(s)
          #print(s)
   print(dates, '\n')
   print(description, '\n')
   print(amounts, '\n')       

   #print(a_list)



  #print(type(a_list))

 getting_dates_description_amount_separately()

 import pickle

 with open('untitled19.pkl', 'wb') as f:
    pickle.dump(model,f)

  import csv

 with open('mycsv.csv', 'w', newline='') as f:
    fieldname = ['Dates', 'Description', 'Amount']
    thewriter = csv.DictWriter(f, fieldnames=fieldname)


  thewriter.writeheader()
  thewriter.writerow({'Dates':dates, 'Description': description, 
   'Amount':amounts})

   data_frame = pd.read_csv("mycsv.csv")
   data_frame