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README (1).md

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+---
+title: docTR
+emoji: 📑
+colorFrom: purple
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.30.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio` or `streamlit`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app (1).py

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+# Copyright (C) 2021-2024, Mindee.
+
+# This program is licensed under the Apache License 2.0.
+# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+import torch
+
+from doctr.io import DocumentFile
+from doctr.utils.visualization import visualize_page
+
+from backend.pytorch import DET_ARCHS, RECO_ARCHS, forward_image, load_predictor
+
+forward_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+def main(det_archs, reco_archs):
+    """Build a streamlit layout"""
+    # Wide mode
+    st.set_page_config(layout="wide")
+
+    # Designing the interface
+    st.title("docTR: Document 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 page")
+    cols[1].subheader("Segmentation heatmap")
+    cols[2].subheader("OCR output")
+    cols[3].subheader("Page reconstitution")
+
+    # Sidebar
+    # File selection
+    st.sidebar.title("Document selection")
+    # Choose your own image
+    uploaded_file = st.sidebar.file_uploader("Upload files", type=["pdf", "png", "jpeg", "jpg"])
+    if uploaded_file is not None:
+        if uploaded_file.name.endswith(".pdf"):
+            doc = DocumentFile.from_pdf(uploaded_file.read())
+        else:
+            doc = DocumentFile.from_images(uploaded_file.read())
+        page_idx = st.sidebar.selectbox("Page selection", [idx + 1 for idx in range(len(doc))]) - 1
+        page = doc[page_idx]
+        cols[0].image(page)
+
+    # Model selection
+    st.sidebar.title("Model selection")
+    det_arch = st.sidebar.selectbox("Text detection model", det_archs)
+    reco_arch = st.sidebar.selectbox("Text recognition model", reco_archs)
+
+    # For newline
+    st.sidebar.write("\n")
+    # Only straight pages or possible rotation
+    st.sidebar.title("Parameters")
+    assume_straight_pages = st.sidebar.checkbox("Assume straight pages", value=True)
+    st.sidebar.write("\n")
+    # Straighten pages
+    straighten_pages = st.sidebar.checkbox("Straighten pages", value=False)
+    st.sidebar.write("\n")
+    # Binarization threshold
+    bin_thresh = st.sidebar.slider("Binarization threshold", min_value=0.1, max_value=0.9, value=0.3, step=0.1)
+    st.sidebar.write("\n")
+
+    if st.sidebar.button("Analyze page"):
+        if uploaded_file is None:
+            st.sidebar.write("Please upload a document")
+
+        else:
+            with st.spinner("Loading model..."):
+                predictor = load_predictor(
+                    det_arch, reco_arch, assume_straight_pages, straighten_pages, bin_thresh, forward_device
+                )
+
+            with st.spinner("Analyzing..."):
+                # Forward the image to the model
+                seg_map = forward_image(predictor, page, forward_device)
+                seg_map = np.squeeze(seg_map)
+                seg_map = cv2.resize(seg_map, (page.shape[1], page.shape[0]), interpolation=cv2.INTER_LINEAR)
+
+                # Plot the raw heatmap
+                fig, ax = plt.subplots()
+                ax.imshow(seg_map)
+                ax.axis("off")
+                cols[1].pyplot(fig)
+
+                # Plot OCR output
+                out = predictor([page])
+                fig = visualize_page(out.pages[0].export(), out.pages[0].page, interactive=False, add_labels=False)
+                cols[2].pyplot(fig)
+
+                # Page reconsitution under input page
+                page_export = out.pages[0].export()
+                if assume_straight_pages or (not assume_straight_pages and straighten_pages):
+                    img = out.pages[0].synthesize()
+                    cols[3].image(img, clamp=True)
+
+                # Display JSON
+                st.markdown("\nHere are your analysis results in JSON format:")
+                st.json(page_export, expanded=False)
+
+
+if __name__ == "__main__":
+    main(DET_ARCHS, RECO_ARCHS)

backend_pytorch.py

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+# Copyright (C) 2021-2024, Mindee.
+
+# This program is licensed under the Apache License 2.0.
+# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
+
+import numpy as np
+import torch
+
+from doctr.models import ocr_predictor
+from doctr.models.predictor import OCRPredictor
+
+DET_ARCHS = [
+    "db_resnet50",
+    "db_resnet34",
+    "db_mobilenet_v3_large",
+    "linknet_resnet18",
+    "linknet_resnet34",
+    "linknet_resnet50",
+]
+RECO_ARCHS = [
+    "crnn_vgg16_bn",
+    "crnn_mobilenet_v3_small",
+    "crnn_mobilenet_v3_large",
+    "master",
+    "sar_resnet31",
+    "vitstr_small",
+    "vitstr_base",
+    "parseq",
+]
+
+
+def load_predictor(
+    det_arch: str,
+    reco_arch: str,
+    assume_straight_pages: bool,
+    straighten_pages: bool,
+    bin_thresh: float,
+    device: torch.device,
+) -> OCRPredictor:
+    """Load a predictor from doctr.models
+
+    Args:
+    ----
+        det_arch: detection architecture
+        reco_arch: recognition architecture
+        assume_straight_pages: whether to assume straight pages or not
+        straighten_pages: whether to straighten rotated pages or not
+        bin_thresh: binarization threshold for the segmentation map
+        device: torch.device, the device to load the predictor on
+
+    Returns:
+    -------
+        instance of OCRPredictor
+    """
+    predictor = ocr_predictor(
+        det_arch,
+        reco_arch,
+        pretrained=True,
+        assume_straight_pages=assume_straight_pages,
+        straighten_pages=straighten_pages,
+        export_as_straight_boxes=straighten_pages,
+        detect_orientation=not assume_straight_pages,
+    ).to(device)
+    predictor.det_predictor.model.postprocessor.bin_thresh = bin_thresh
+    return predictor
+
+
+def forward_image(predictor: OCRPredictor, image: np.ndarray, device: torch.device) -> np.ndarray:
+    """Forward an image through the predictor
+
+    Args:
+    ----
+        predictor: instance of OCRPredictor
+        image: image to process
+        device: torch.device, the device to process the image on
+
+    Returns:
+    -------
+        segmentation map
+    """
+    with torch.no_grad():
+        processed_batches = predictor.det_predictor.pre_processor([image])
+        out = predictor.det_predictor.model(processed_batches[0].to(device), return_model_output=True)
+        seg_map = out["out_map"].to("cpu").numpy()
+
+    return seg_map

gitattributes (1)

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packages.txt

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+python3-opencv

requirements (1).txt

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+-e git+https://github.com/mindee/doctr.git#egg=python-doctr[torch]
+streamlit>=1.0.0