File size: 3,146 Bytes
1569310 3878fb6 1569310 2d7b88a c82795d 1569310 93fe459 9e79a73 1569310 9e79a73 1569310 98c7b0e 1569310 c0ff73d 5454a24 c8cfd54 13ef7e9 69f9615 c8cfd54 13ef7e9 c8cfd54 ecdecda 1569310 c10e31c 1569310 8439022 c10e31c 1569310 6576c4b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
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)
return text_output
# flag(Method,text_output,img)
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 = """<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(enable_queue = False)
|