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c1b1f3a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import os
import time
from typing import List, Dict, Any
import numpy as np
import pandas as pd
import mlflow
from .ocr_space_engine import OCRSpaceEngine
def compute_cer(reference: str, hypothesis: str) -> float:
"""Computes Character Error Rate (CER) via edit distance."""
r = reference.lower().strip()
h = hypothesis.lower().strip()
if not r:
return 0.0 if not h else 1.0
n, m = len(r), len(h)
dp = [[0] * (m + 1) for _ in range(n + 1)]
for i in range(n + 1):
dp[i][0] = i
for j in range(m + 1):
dp[0][j] = j
for i in range(1, n + 1):
for j in range(1, m + 1):
if r[i - 1] == h[j - 1]:
dp[i][j] = dp[i - 1][j - 1]
else:
dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
dist = dp[n][m]
return float(dist / max(len(r), 1))
def run_ocr_comparison(
sample_records: List[Any],
cache_dir: str = "data/processed/ocr_cache",
tracking_uri: str = "sqlite:///mlflow.db"
) -> pd.DataFrame:
"""
Runs comparison between OCR engines and logs results to MLflow under 'ocr_comparison'.
"""
mlflow.set_tracking_uri(tracking_uri)
exp = mlflow.set_experiment("ocr_comparison")
engines = {
"ocr_space_engine2": OCRSpaceEngine(ocr_engine="2", cache_dir=cache_dir),
"ocr_space_engine1": OCRSpaceEngine(ocr_engine="1", cache_dir=cache_dir)
}
comparison_rows = []
with mlflow.start_run(run_name="ocr_engine_benchmark", experiment_id=exp.experiment_id):
for name, engine in engines.items():
runtimes = []
confidences = []
cers = []
for rec in sample_records:
img_path = rec.image_path if hasattr(rec, "image_path") else rec.get("image_path")
doc_id = rec.doc_id if hasattr(rec, "doc_id") else rec.get("doc_id")
gt_words = rec.ocr_words if hasattr(rec, "ocr_words") else rec.get("ocr_words", [])
gt_text = " ".join([w.text if hasattr(w, "text") else w.get("text", "") for w in gt_words])
t0 = time.time()
words, _ = engine.extract_with_cache(img_path, doc_id=doc_id)
dur = time.time() - t0
runtimes.append(dur)
if words:
confs = [w.conf for w in words]
confidences.append(float(np.mean(confs)))
pred_text = " ".join([w.text for w in words])
if gt_text:
cers.append(compute_cer(gt_text, pred_text))
else:
cers.append(0.0)
else:
confidences.append(0.0)
cers.append(1.0)
avg_runtime = float(np.mean(runtimes)) if runtimes else 0.0
avg_conf = float(np.mean(confidences)) if confidences else 0.0
avg_cer = float(np.mean(cers)) if cers else 0.0
mlflow.log_metric(f"{name}_avg_runtime_sec", avg_runtime)
mlflow.log_metric(f"{name}_avg_confidence", avg_conf)
mlflow.log_metric(f"{name}_cer", avg_cer)
comparison_rows.append({
"engine": name,
"avg_runtime_sec": round(avg_runtime, 4),
"avg_confidence": round(avg_conf, 4),
"cer": round(avg_cer, 4)
})
df_res = pd.DataFrame(comparison_rows)
csv_path = "reports/tables/ocr_engine_comparison.csv"
os.makedirs(os.path.dirname(csv_path), exist_ok=True)
df_res.to_csv(csv_path, index=False)
mlflow.log_artifact(csv_path)
return df_res
if __name__ == "__main__":
from ..data.schema import DocumentRecord, OCRWord
dummy_records = [
DocumentRecord(
doc_id="sample_0",
source_dataset="test",
doc_class="invoice",
image_path="",
ocr_words=[OCRWord(text="INVOICE TOTAL 100", bbox=[0, 0, 10, 10], conf=1.0)],
fields={}
)
]
df = run_ocr_comparison(dummy_records)
print(df)
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