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import easyocr as ocr #OCR | |
import streamlit as st #Web App | |
from PIL import Image #Image Processing | |
import numpy as np #Image Processing | |
#title | |
st.title("Easy OCR - Extract Text from Images") | |
#subtitle | |
st.markdown("## Optical Character Recognition - Using `easyocr`, `streamlit` - hosted on π€ Spaces") | |
st.markdown("Link to the app - [image-to-text-app on π€ Spaces](https://huggingface.co/spaces/Amrrs/image-to-text-app)") | |
#image uploader | |
image = st.file_uploader(label = "Upload your image here",type=['png','jpg','jpeg']) | |
def load_model(): | |
reader = ocr.Reader(['en'],model_storage_directory='.') | |
return reader | |
reader = load_model() #load model | |
if image is not None: | |
input_image = Image.open(image) #read image | |
st.image(input_image) #display image | |
with st.spinner("π€ AI is at Work! "): | |
result = reader.readtext(np.array(input_image)) | |
result_text = [] #empty list for results | |
for text in result: | |
result_text.append(text[1]) | |
st.write(result_text) | |
#st.success("Here you go!") | |
st.balloons() | |
else: | |
st.write("Upload an Image") | |
st.caption("Made with β€οΈ by @1littlecoder. Credits to π€ Spaces for Hosting this ") | |