Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM:
legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint,
a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual
example outputs, and quantization/concurrency benchmarks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

surya/detection/util.py

@@ -0,0 +1,36 @@
+import math
+from PIL import ImageOps
+
+from surya.settings import settings
+
+
+def get_total_splits(image_size, height):
+    img_height = list(image_size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / height)
+        return num_splits
+    return 1
+
+
+def split_image(img, height):
+    # This will not modify/return the original image - it will either crop, or copy the image
+    img_height = list(img.size)[1]
+    max_height = settings.DETECTOR_IMAGE_CHUNK_HEIGHT
+    if img_height > max_height:
+        num_splits = math.ceil(img_height / height)
+        splits = []
+        split_heights = []
+        for i in range(num_splits):
+            top = i * height
+            bottom = (i + 1) * height
+            if bottom > img_height:
+                bottom = img_height
+            cropped = img.crop((0, top, img.size[0], bottom))
+            chunk_height = bottom - top
+            if chunk_height < height:
+                cropped = ImageOps.pad(cropped, (img.size[0], height), color=255, centering=(0, 0))
+            splits.append(cropped)
+            split_heights.append(chunk_height)
+        return splits, split_heights
+    return [img.copy()], [img_height]