import gradio as gr import requests import tensorflow as tf import keras_ocr 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) # img_path = 'exp.jpeg' 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 """ # gray scale image def get_grayscale(image): return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Thresholding or Binarization 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) flag(Method,text_output,img) return text_output else: raise gr.Error("Please upload an image!!!!") # except Exception as e: # print("Error in ocr generation ==>",e) # text_output = "Something went wrong" # return text_output """ 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 = """

Feel free to give us your thoughts on this demo and please contact us at letstalk@pragnakalp.com.

Developed by: Pragnakalp Techlabs

""" ) demo.launch(enable_queue = False)