pip install pytesseract Pillow

from PIL import Image
import pytesseract

# 打開圖片
img = Image.open("example.png")

# 進行文字識別
text = pytesseract.image_to_string(img)

# 打印識別結(jié)果
print("識別結(jié)果：", text)

# 指定語言為中文
text_chinese = pytesseract.image_to_string(img, lang='chi_sim')

# 打印中文識別結(jié)果
print("中文識別結(jié)果：", text_chinese)

from PIL import ImageEnhance

# 增強圖片對比度
enhancer = ImageEnhance.Contrast(img)
img_contrast = enhancer.enhance(2.0)

# 進行文字識別
text_contrast = pytesseract.image_to_string(img_contrast)

# 打印識別結(jié)果
print("增強對比度后的識別結(jié)果：", text_contrast)

# 指定配置，開啟多列文字處理
custom_config = r'--oem 3 --psm 6'
text_multi_column = pytesseract.image_to_string(img, config=custom_config)

# 打印多列文字識別結(jié)果
print("多列文字識別結(jié)果：", text_multi_column)

import easyocr

# 創(chuàng)建OCR對象
reader = easyocr.Reader(['ch_sim', 'en'])

# 進行文字識別
result = reader.readtext('example.png')

# 打印識別結(jié)果
for detection in result:
    print("識別結(jié)果：", detection[1])

import tesserocr
from sklearn.externals import joblib

# 載入機器學(xué)習(xí)模型
model = joblib.load('text_recognition_model.joblib')

# 進行文字識別
text_ml = tesserocr.image_to_text(img, model=model)

# 打印機器學(xué)習(xí)文字識別結(jié)果
print("機器學(xué)習(xí)文字識別結(jié)果：", text_ml)

custom_config_optimized = r'--oem 3 --psm 6 -c tessedit_char_whitelist=abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'

# 進行優(yōu)化后的文字識別
text_optimized = pytesseract.image_to_string(img, config=custom_config_optimized)
print("優(yōu)化后的識別結(jié)果：", text_optimized)

# 將識別結(jié)果保存到文本文件
with open('text_output.txt', 'w', encoding='utf-8') as file:
    file.write(text)

# 指定配置，開啟表格文字處理
custom_config_table = r'--oem 3 --psm 6 -c preserve_interword_spaces=1'
text_table = pytesseract.image_to_string(img_table, config=custom_config_table)

# 打印表格文字識別結(jié)果
print("表格文字識別結(jié)果：", text_table)

from handwriting_recognition import HandwritingRecognition

# 創(chuàng)建手寫體文字識別對象
handwriting_recognizer = HandwritingRecognition()

# 進行手寫體文字識別
text_handwriting = handwriting_recognizer.recognize(img_handwriting)

# 打印手寫體文字識別結(jié)果
print("手寫體文字識別結(jié)果：", text_handwriting)

from tensorflow import keras
from keras_ocr.detection import Detector
from keras_ocr.recognition import Recognizer
import cv2

# 載入預(yù)訓(xùn)練模型
detector = Detector()
recognizer = Recognizer()

# 進行文字檢測
boxes = detector.detect(images=[cv2.imread('example.png')])

# 進行文字識別
prediction = recognizer.recognize(images=[cv2.imread('example.png')], detection_boxes=boxes)

# 打印深度學(xué)習(xí)模型文字識別結(jié)果
print("深度學(xué)習(xí)模型文字識別結(jié)果：", prediction[0][0]['text'])

from flask import Flask, request, jsonify
import pytesseract
from PIL import Image

app = Flask(__name__)

@app.route('/recognize', methods=['POST'])
def recognize_text():
    # 接收上傳的圖片
    file = request.files['image']
    
    # 保存圖片
    file.save('uploaded_image.png')
    
    # 進行文字識別
    img = Image.open('uploaded_image.png')
    text = pytesseract.image_to_string(img)

    return jsonify({'text': text})

if __name__ == '__main__':
    app.run(debug=True)