| import gradio as gr |
| import tensorflow as tf |
| import keras_ocr |
| import requests |
| import cv2 |
| import os |
| import csv |
| import numpy as np |
| import pandas as pd |
| import huggingface_hub |
| from huggingface_hub import Repository |
| from datetime import datetime |
| import scipy.ndimage.interpolation as inter |
| import easyocr |
| import datasets |
| from datasets import load_dataset, Image |
| from PIL import Image |
| from paddleocr import PaddleOCR |
| from save_data import flag |
| |
| """ |
| Paddle OCR |
| """ |
| def ocr_with_paddle(img): |
| finaltext = '' |
| ocr = PaddleOCR(lang='en', use_angle_cls=True) |
| |
| result = ocr.ocr(img) |
| |
| for i in range(len(result[0])): |
| text = result[0][i][1][0] |
| finaltext += ' '+ text |
| return finaltext |
|
|
| """ |
| Keras OCR |
| """ |
| def ocr_with_keras(img): |
| output_text = '' |
| pipeline=keras_ocr.pipeline.Pipeline() |
| images=[keras_ocr.tools.read(img)] |
| predictions=pipeline.recognize(images) |
| first=predictions[0] |
| for text,box in first: |
| output_text += ' '+ text |
| return output_text |
|
|
| """ |
| easy OCR |
| """ |
| |
| def get_grayscale(image): |
| return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
|
|
| |
| def thresholding(src): |
| return cv2.threshold(src,127,255, cv2.THRESH_TOZERO)[1] |
| def ocr_with_easy(img): |
| gray_scale_image=get_grayscale(img) |
| thresholding(gray_scale_image) |
| cv2.imwrite('image.png',gray_scale_image) |
| reader = easyocr.Reader(['th','en']) |
| bounds = reader.readtext('image.png',paragraph="False",detail = 0) |
| bounds = ''.join(bounds) |
| return bounds |
| |
| """ |
| Generate OCR |
| """ |
| def generate_ocr(Method,img): |
| |
| text_output = '' |
| if (img).any(): |
| add_csv = [] |
| image_id = 1 |
| print("Method___________________",Method) |
| if Method == 'EasyOCR': |
| text_output = ocr_with_easy(img) |
| if Method == 'KerasOCR': |
| text_output = ocr_with_keras(img) |
| if Method == 'PaddleOCR': |
| text_output = ocr_with_paddle(img) |
| |
| try: |
| flag(Method,text_output,img) |
| except Exception as e: |
| print(e) |
| return text_output |
| else: |
| raise gr.Error("Please upload an image!!!!") |
| |
| |
| |
| |
| |
| |
|
|
| """ |
| Create user interface for OCR demo |
| """ |
|
|
| image = gr.Image(shape=(300, 300)) |
| |
| method = gr.Radio(["PaddleOCR","EasyOCR", "KerasOCR"],value="PaddleOCR") |
| output = gr.Textbox(label="Output") |
|
|
| demo = gr.Interface( |
| generate_ocr, |
| [method,image], |
| output, |
| title="Optical Character Recognition", |
| css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}", |
| article = """<p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at |
| <a href="mailto:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</a> |
| <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>""" |
| |
|
|
| ) |
| demo.launch(enable_queue = False) |
| |
|
|
