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>
2026-06-17 10:20:02 +04:00
commit 1a585693be
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+import os
+import click
+import copy
+import json
+from collections import defaultdict
+
+from surya.common.util import expand_bbox
+from surya.debug.draw import draw_bboxes_on_image
+from surya.inference import SuryaInferenceManager
+from surya.layout import LayoutPredictor
+from surya.logging import configure_logging, get_logger
+from surya.scripts.config import CLILoader
+from surya.table_rec import TableRecPredictor
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="Run table recognition on an input file or folder.")
+@CLILoader.common_options
+@click.option(
+    "--skip_table_detection",
+    is_flag=True,
+    help="Tables are already cropped, so don't re-detect tables.",
+    default=False,
+)
+@click.option(
+    "--mode",
+    type=click.Choice(["simple", "full"]),
+    default="simple",
+    help="simple: rows+cols only (geometric cells). full: full HTML (BLOCK_PROMPT).",
+)
+def table_recognition_cli(
+    input_path: str, skip_table_detection: bool, mode: str, **kwargs
+):
+    # Layout runs on the low-DPI render; table crops come from the high-DPI
+    # image so the table_rec model sees readable cell content.
+    loader = CLILoader(input_path, kwargs, highres=True)
+
+    manager = SuryaInferenceManager()
+    layout_predictor = LayoutPredictor(manager)
+    table_rec_predictor = TableRecPredictor(manager)
+
+    pnums = []
+    prev_name = None
+    for name in loader.names:
+        if prev_name is None or prev_name != name:
+            pnums.append(0)
+        else:
+            pnums.append(pnums[-1] + 1)
+        prev_name = name
+
+    table_imgs = []
+    table_counts = []
+    table_counts_per_img = []
+
+    if skip_table_detection:
+        for img in loader.highres_images:
+            table_imgs.append(img)
+            table_counts.append(1)
+            table_counts_per_img.append(0)
+    else:
+        layout_predictions = layout_predictor(
+            loader.images,
+            target_image_sizes=[img.size for img in loader.highres_images],
+        )
+        for layout_pred, img in zip(layout_predictions, loader.highres_images):
+            tables_on_page = [
+                line
+                for line in layout_pred.bboxes
+                if line.label in ("Table", "TableOfContents")
+            ]
+            table_counts.append(len(tables_on_page))
+            for line in tables_on_page:
+                bbox = expand_bbox(line.bbox)
+                table_imgs.append(img.crop(bbox))
+                table_counts_per_img.append(line.count)
+
+    table_preds = table_rec_predictor(table_imgs, mode=mode)
+
+    img_idx = 0
+    prev_count = 0
+    table_predictions = defaultdict(list)
+    for i in range(sum(table_counts)):
+        while i >= prev_count + table_counts[img_idx]:
+            prev_count += table_counts[img_idx]
+            img_idx += 1
+
+        pred = table_preds[i]
+        orig_name = loader.names[img_idx]
+        pnum = pnums[img_idx]
+        table_img = table_imgs[i]
+
+        out_pred = pred.model_dump()
+        out_pred["page"] = pnum + 1
+        table_idx = i - prev_count
+        out_pred["table_idx"] = table_idx
+        table_predictions[orig_name].append(out_pred)
+
+        if loader.save_images and pred.rows:
+            rows = [line.bbox for line in pred.rows]
+            cols = [line.bbox for line in pred.cols]
+            row_labels = [f"Row {line.row_id}" for line in pred.rows]
+            col_labels = [f"Col {line.col_id}" for line in pred.cols]
+            cells = [line.bbox for line in pred.cells]
+
+            rc_image = copy.deepcopy(table_img)
+            rc_image = draw_bboxes_on_image(
+                rows, rc_image, labels=row_labels, label_font_size=20, color="blue"
+            )
+            rc_image = draw_bboxes_on_image(
+                cols, rc_image, labels=col_labels, label_font_size=20, color="red"
+            )
+            rc_image.save(
+                os.path.join(
+                    loader.result_path,
+                    f"{orig_name}_page{pnum + 1}_table{table_idx}_rc.png",
+                )
+            )
+
+            cell_image = copy.deepcopy(table_img)
+            cell_image = draw_bboxes_on_image(cells, cell_image, color="green")
+            cell_image.save(
+                os.path.join(
+                    loader.result_path,
+                    f"{orig_name}_page{pnum + 1}_table{table_idx}_cells.png",
+                )
+            )
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(table_predictions, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
+
+
+if __name__ == "__main__":
+    table_recognition_cli()