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"""Assemble isolated handwriting and Gemini template text in benchmark order."""
from __future__ import annotations
import argparse
import json
import os
import unicodedata
from pathlib import Path
from typing import Any, Callable
from rapidfuzz.distance import Levenshtein
PLACEHOLDER = "{{HANDWRITING}}"
VARIANTS: dict[str, tuple[float | None, bool]] = {
"simple_top": (None, False),
"simple_spread_right": (None, True),
"row055_top": (0.55, False),
"row070_top": (0.70, False),
"row055_spread_right": (0.55, True),
"row070_spread_right": (0.70, True),
}
def normalized(text: str) -> str:
return " ".join(unicodedata.normalize("NFKC", str(text)).split())
def merge_template_text(template_text: str, handwriting: str) -> tuple[str, int]:
handwriting = normalized(handwriting)
count = str(template_text).count(PLACEHOLDER)
if count:
merged = str(template_text).replace(PLACEHOLDER, handwriting, 1)
merged = merged.replace(PLACEHOLDER, "")
elif normalized(template_text):
merged = f"{template_text} {handwriting}"
else:
merged = handwriting
return normalized(merged), count
def center(line: dict[str, Any]) -> tuple[float, float]:
x0, y0, x1, y1 = (float(value) for value in line["bbox"])
return (x0 + x1) / 2.0, (y0 + y1) / 2.0
def height(line: dict[str, Any]) -> float:
return max(float(line["bbox"][3]) - float(line["bbox"][1]), 1.0)
def simple(lines: list[dict[str, Any]]) -> list[list[dict[str, Any]]]:
return [[line] for line in sorted(lines, key=lambda item: (center(item)[1], -center(item)[0]))]
def clustered(lines: list[dict[str, Any]], factor: float) -> list[list[dict[str, Any]]]:
ordered = sorted(lines, key=lambda item: center(item)[1])
rows: list[list[dict[str, Any]]] = []
for line in ordered:
if not rows:
rows.append([line])
continue
row = rows[-1]
mean_y = sum(center(item)[1] for item in row) / len(row)
mean_height = sum(height(item) for item in row) / len(row)
if abs(center(line)[1] - mean_y) <= factor * max(height(line), mean_height):
row.append(line)
else:
rows.append([line])
for row in rows:
row.sort(key=lambda item: center(item)[0], reverse=True)
return rows
def layout(
lines: list[dict[str, Any]],
width: int,
height_value: int,
factor: float | None,
spread: bool,
) -> list[list[dict[str, Any]]]:
builder: Callable[[list[dict[str, Any]]], list[list[dict[str, Any]]]]
builder = simple if factor is None else lambda value: clustered(value, factor)
aspect = width / max(height_value, 1)
if spread and 1.03 <= aspect <= 1.42:
midpoint = width / 2.0
return builder([line for line in lines if center(line)[0] >= midpoint]) + builder(
[line for line in lines if center(line)[0] < midpoint]
)
return builder(lines)
def handwriting_prediction(row: dict[str, Any], variant: str) -> str:
factor, spread = VARIANTS[variant]
rows = layout(
row.get("lines", []),
int(row.get("width") or 0),
int(row.get("height") or 0),
factor,
spread,
)
return "\n".join(
" ".join(
str(line.get("text") or "").strip()
for line in group
if str(line.get("text") or "").strip()
)
for group in rows
if any(str(line.get("text") or "").strip() for line in group)
)
def metric(reference: str, hypothesis: str) -> dict[str, int]:
reference = normalized(reference)
hypothesis = normalized(hypothesis)
return {
"char_errors": int(Levenshtein.distance(reference, hypothesis)),
"ref_chars": len(reference),
"word_errors": int(
Levenshtein.distance(reference.split(), hypothesis.split())
),
"ref_words": len(reference.split()),
}
def aggregate(rows: list[dict[str, Any]]) -> dict[str, float | int]:
totals = {key: 0 for key in ("char_errors", "ref_chars", "word_errors", "ref_words")}
for row in rows:
for key, value in metric(row["reference"], row["prediction"]).items():
totals[key] += value
return {
**totals,
"wer": totals["word_errors"] / max(totals["ref_words"], 1),
"cer": totals["char_errors"] / max(totals["ref_chars"], 1),
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--recognized-lines", type=Path, required=True)
parser.add_argument("--baseline-predictions", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--isolation-variant", required=True)
args = parser.parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
recognized = [
json.loads(line)
for line in args.recognized_lines.read_text(encoding="utf-8").splitlines()
if line.strip()
]
selected_ids = {str(row["id"]) for row in recognized}
baseline = [
json.loads(line)
for line in args.baseline_predictions.read_text(encoding="utf-8").splitlines()
if line.strip() and str(json.loads(line).get("id")) in selected_ids
]
baseline_metrics = aggregate(baseline)
candidates = {}
for layout_variant in VARIANTS:
rows = []
missing_placeholders = 0
multiple_placeholders = 0
for row in recognized:
handwriting = handwriting_prediction(row, layout_variant)
prediction, placeholders = merge_template_text(
str(row.get("template_text") or ""),
handwriting,
)
missing_placeholders += int(placeholders == 0)
multiple_placeholders += int(placeholders > 1)
rows.append(
{
"id": row["id"],
"split": "handwriting",
"reference": row["reference"],
"prediction": prediction,
"handwriting_prediction": normalized(handwriting),
"template_id": row.get("template_id"),
"template_placeholder_count": placeholders,
"isolation_variant": args.isolation_variant,
"layout_variant": layout_variant,
"detected_lines": len(row.get("lines", [])),
**({"error": row["error"]} if row.get("error") else {}),
}
)
path = args.output_dir / f"predictions-{layout_variant}.jsonl"
temporary = path.with_suffix(".jsonl.tmp")
with temporary.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
os.replace(temporary, path)
candidates[layout_variant] = {
**aggregate(rows),
"missing_placeholders": missing_placeholders,
"multiple_placeholders": multiple_placeholders,
"path": str(path),
}
best = min(
candidates,
key=lambda name: (
candidates[name]["wer"] + candidates[name]["cer"],
candidates[name]["cer"],
),
)
summary = {
"isolation_variant": args.isolation_variant,
"rows": len(recognized),
"baseline": baseline_metrics,
"candidates": candidates,
"best_layout_variant": best,
"best": candidates[best],
}
(args.output_dir / "assembly-summary.json").write_text(
json.dumps(summary, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)
print(json.dumps(summary, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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