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Building
| 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 🤗 Spaces") | |
| st.markdown("Link to the app - [image-to-text-app on 🤗 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("🤖 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 ❤️ by @1littlecoder. Credits to 🤗 Spaces for Hosting this ") | |