Add application file

app.py

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+import streamlit as st
+from kraken import blla, rpred
+from kraken.lib import vgsl
+from kraken.lib import models
+from PIL import Image, ImageDraw
+
+# Define available OCR models for segmentation and recognition
+segmentation_models = {
+    "1col_442_sam_v1.mlmodel": "models/1col_442_sam_v1.mlmodel",
+    "ubma_sam_v4.mlmodel": "models/ubma_sam_v4.mlmodel"
+}
+
+recognition_models = {
+    "sinai_sam_rec_v4.mlmodel": "models/sinai_sam_rec_v4.mlmodel",
+    "sinai_sam_rec_v2.mlmodel": "models/sinai_sam_rec_v2.mlmodel"
+}
+
+# Streamlit app title and description
+st.title("OCR with Kraken - Segmentation and Recognition")
+st.write("Upload an image, select segmentation and recognition models, and view OCR results.")
+
+# Upload image file
+uploaded_image = st.file_uploader("Upload an image file", type=["png", "jpg", "jpeg"])
+
+# Select segmentation and recognition models
+selected_seg_model = st.selectbox("Select Kraken Segmentation Model", list(segmentation_models.keys()))
+selected_rec_model = st.selectbox("Select Kraken Recognition Model", list(recognition_models.keys()))
+
+# Option to draw baselines
+draw_baselines = st.radio("Options", ("Do not draw baselines", "Draw baselines")) == "Draw baselines"
+
+# Process the image if uploaded and models selected
+if uploaded_image and selected_seg_model and selected_rec_model:
+    # Load the image
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Load selected Kraken segmentation and recognition models
+    seg_model_path = segmentation_models[selected_seg_model]
+    rec_model_path = recognition_models[selected_rec_model]
+    seg_model = vgsl.TorchVGSLModel.load_model(seg_model_path)
+    rec_model = models.load_any(rec_model_path)
+
+    # Segment image using Kraken segmentation model
+    baseline_seg = blla.segment(image, model=seg_model)
+
+    # Pass segmentation result to recognition model
+    pred_it = rpred.rpred(network=rec_model, im=image, bounds=baseline_seg)
+
+    # Prepare to draw boundaries and display info
+    boundaries_info = []
+    draw = ImageDraw.Draw(image)
+
+    # Process recognition predictions for lines and draw on image
+    for idx, pred in enumerate(pred_it):
+        prediction = pred.prediction
+        line_boundary = [(int(x), int(y)) for x, y in pred.boundary]
+        line_baseline = [(int(x), int(y)) for x, y in pred.baseline] if pred.baseline else None
+        line_type = pred.tags.get("type", "undefined")  # Get line type dynamically if available
+
+        # Add boundary, baseline (if selected), and prediction to display info in the new order
+        boundaries_info.append(f"**Line {idx + 1}** (type: {line_type}):\n  - Boundary: {line_boundary}")
+
+        # Draw boundary in green
+        draw.polygon(line_boundary, outline="green")
+
+        # Draw baseline if the option is selected and add it to display info
+        if draw_baselines and line_baseline:
+            boundaries_info.append(f"  - Baseline: {line_baseline}")
+            draw.line(line_baseline, fill="red", width=2)  # Draw baseline in red
+
+        # Add prediction last
+        boundaries_info.append(f"  - Prediction: {prediction}")
+
+    # Process and draw region boundaries from baseline_seg
+    for region_type, region_list in baseline_seg.regions.items():
+        for idx, region_data in enumerate(region_list):
+            if hasattr(region_data, "boundary"):
+                region_boundary = [(int(x), int(y)) for x, y in region_data.boundary]
+                region_type_name = region_data.tags.get("type", region_type)  # Get region type dynamically
+                boundaries_info.append(f"**Region {idx + 1}** (type: {region_type_name}):\n  - Boundary: {region_boundary}")
+                draw.polygon(region_boundary, outline="blue")  # Draw region boundary in blue
+
+    # Display the image with boundaries drawn
+    st.image(image, caption="Image with OCR boundaries (green for lines, blue for regions), baselines (red if selected)", use_column_width=True)
+
+    # Display the list of boundaries, predictions, and baselines
+    st.write("**List of Boundaries, Predictions, and Baselines (if selected):**")
+    for info in boundaries_info:
+        st.write(info)
+

models/1col_442_sam_v1.mlmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:22e0b673d48337aef6ca29279418733fb861108d79ef7b3718b4c1f16eb7740e
+size 5219282

models/sinai_sam_rec_v2.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7af04abf44199e3bbb99afa2fe77f7dd6f78c4621863459b57f0e34f67a1a38c
+size 16024546

models/sinai_sam_rec_v4.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38f24382398e311d0babedab68d064b7eae8323ca9e3a2ed389102e1539926b4
+size 16029821

models/ubma_sam_v4.mlmodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:900147a3c02a0b3877667eef76bba13d074b6c529013bc8aa001faf75f45e40c
+size 5075650

requirements.txt

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+kraken
+pillow
+streamlit