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| from PIL import Image | |
| import pytesseract | |
| import easyocr | |
| import cv2 | |
| import os | |
| from io import BytesIO | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| DET_ARCHS = ["pytesseract", "easyocr"] | |
| def main(): | |
| # Wide mode | |
| st.set_page_config(layout="wide") | |
| # Designing the interface | |
| st.title("Image Text Recognition") | |
| # For newline | |
| st.write('\n') | |
| # Instructions | |
| st.markdown("*Hint: click on the top-right corner of an image to enlarge it!*") | |
| # Set the columns | |
| cols = st.columns((1, 1, 1, 1)) | |
| cols[0].subheader("Input image") | |
| cols[1].subheader("OCR output") | |
| # Sidebar | |
| # File selection | |
| st.sidebar.title("Document selection") | |
| # Disabling warning | |
| # st.set_option('deprecation.showfileUploaderEncoding', False) | |
| # Choose your own image | |
| uploaded_file = st.sidebar.file_uploader("Upload files", type=['png', 'jpeg', 'jpg']) | |
| if uploaded_file is not None: | |
| doc = uploaded_file.read() | |
| cols[0].image(doc) | |
| # Model selection | |
| st.sidebar.title("Model selection") | |
| det_arch = st.sidebar.selectbox("OCR model", DET_ARCHS) | |
| # For newline | |
| st.sidebar.write('\n') | |
| if st.sidebar.button("Analyze image"): | |
| if uploaded_file is None: | |
| st.sidebar.write("Please upload an image") | |
| else: | |
| with st.spinner("Loading model..."): | |
| if det_arch == 'pytesseract': | |
| predictor = pytesseract.image_to_string(Image.open(BytesIO(doc))) | |
| else: | |
| reader = easyocr.Reader(['en']) | |
| predictor = reader.readtext(doc, detail = 0) | |
| with st.spinner('Analyzing...'): | |
| # Plot OCR output | |
| if det_arch == 'pytesseract': | |
| cols[1].text(predictor) | |
| else: | |
| cols[1].text(''.join(predictor)) | |
| if __name__ == '__main__': | |
| main() | |
| # pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| # def predict(input_img): | |
| # predictions = pipeline(input_img) | |
| # return input_img, {p["label"]: p["score"] for p in predictions} | |
| # def recognize(input_img): | |
| # text = pytesseract.image_to_string(Image.open("./data/" + filename)) | |
| # return input_img, text | |
| # gradio_app = gr.Interface( | |
| # recognize, | |
| # inputs=[gr.Image(label="Upload an Image", type="pil")], | |
| # outputs=[gr.Textbox(label="Text in the Image")], | |
| # title="Extrate Text From Image", | |
| # ) | |
| # if __name__ == "__main__": | |
| # gradio_app.launch(server_port=8756) | |