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Browse files- README.en.md +18 -0
- README.es.md +16 -0
- README.eu.md +16 -0
- app.py +84 -0
- requirements.txt +5 -0
README.en.md
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## Image2OCR
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## Slow
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If you get this message, it will take a long time.
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Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.
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## Contributions
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Contributions are always welcome!
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## Donations
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[](https://www.buymeacoffee.com/Artgen)
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README.es.md
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## Image2OCR (im谩gene a reconocimiento 贸ptico de caracteres)
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## Lento
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Si recibes este mensaje, tomar谩 mucho tiempo.
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Ni CUDA ni MPS est谩n disponibles; se est谩 utilizando la CPU por defecto. Nota: Este m贸dulo es mucho m谩s r谩pido con una GPU.
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## Contribuciones
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隆Las contribuciones son siempre bienvenidas!
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## Donaciones
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[](https://www.buymeacoffee.com/Artgen)
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README.eu.md
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## Image2OCR
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## Mantxo
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Mezu hau jasotzen baduzu, luze joko du.
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Ez CUDA ez MPS ezin dira ordaindu. Modulu hau askoz azkarragoa da GPU batekin.
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## Kredituak
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Kredituak beti dira ongi etorriak!
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## Donazioak
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[](https://www.buymeacoffee.com/Artgen)
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app.py
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"""
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@author: idoia lerchundi
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"""
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import urllib.request
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from PIL import Image,ImageFile
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import streamlit as st
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import numpy as np
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import requests
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from io import BytesIO
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import easyocr as ocr
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def local_css(file_name):
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with open(file_name) as f:
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
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st.set_page_config(
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page_title="Streamlit iCodeIdoia - OCR an IMAGE - Extract text from an image",
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page_icon="images/ilpicon1.png",layout="wide",initial_sidebar_state="expanded"
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)
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st.image("images/banner.jpg")
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# ---- LOAD
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local_css("styles/style.css")
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@st.cache_resource
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def load_model():
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reader = ocr.Reader(['en'],model_storage_directory='.')
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return reader
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reader = load_model() #load model
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# ---- TABS
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tab1, tab2 = st.tabs(["Demo","Application"])
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with tab1:
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# Handle first image
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url = "https://https://raw.githubusercontent.com/webdevserv/images_video/main/ocr_sample.jpg"
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st.subheader("OCR an image demo")
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img_description = st.text('Image text will extracted using OCR.')
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if st.button('OCR Demo'):
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response = requests.get(url)
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img = Image.open(BytesIO(response.content))
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st.image(input_image) #display image
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img.load()
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st.image(img) #display image
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with st.spinner("馃攧 OCR in process."):
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result = reader.readtext(np.array(img))
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result_text = [] #empty list
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for text in result:
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result_text.append(text[1])
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st.write(result_text)
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st.balloons()
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else:
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st.write("Upload an image to extract text using OCR.")
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with tab2:
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st.subheader("OCR an image app")
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img_description = st.text('Image text will be extracted using OCR.')
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uploaded_file = st.file_uploader("Upload a image to OCR.", type=['jpg'])
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if uploaded_file is not None:
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img = Image.open(uploaded_file)
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img.load()
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st.image(img) #display image
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with st.spinner("馃攧 OCR in process."):
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result = reader.readtext(np.array(img))
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result_text = [] #empty list for results
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for text in result:
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result_text.append(text[1])
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st.write(result_text)
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st.balloons()
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else:
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st.write("Upload an image to extract text using OCR.")
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requirements.txt
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streamlit
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opencv-python-headless
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numpy
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easyocr
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Pillow
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