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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
"""
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
def generate_ocr(Method, file):
text_output = ''
if isinstance(file, bytes): # Handle file uploaded as bytes
file = io.BytesIO(file)
if file.name.endswith('.pdf'):
# Convert PDF to images
images = convert_from_path(file)
for img in images:
img_np = np.array(img)
text_output += generate_text_from_image(Method, img_np) + "\n"
else:
# Handle image file
img_np = np.array(Image.open(file))
text_output = generate_text_from_image(Method, img_np)
return text_output
def generate_text_from_image(Method, img):
text_output = ''
if Method == 'EasyOCR':
text_output = ocr_with_easy(img)
elif Method == 'KerasOCR':
text_output = ocr_with_keras(img)
elif Method == 'PaddleOCR':
text_output = ocr_with_paddle(img)
return text_output
import gradio as gr
image_or_pdf = gr.File(label="Upload an image or PDF")
method = gr.Radio(["PaddleOCR", "EasyOCR", "KerasOCR"], value="PaddleOCR")
output = gr.Textbox(label="Output")
demo = gr.Interface(
generate_ocr,
[method, image_or_pdf],
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:[email protected]" target="_blank">[email protected]</a>
<p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
)
demo.launch(show_error=True)
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