docs: refresh benchmark table with current numbers

Re-ran opendataloader-bench on current main. pdf-inspector improved
across the board since the last table: overall 0.78→0.83, tables
0.59→0.66, headings 0.57→0.74 (competitor rows unchanged). Updated the
prose — heading detection no longer lags opendataloader, and overall is
now within 0.01 of it at ~2.5× the speed.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -26,16 +26,16 @@ Evaluated on the [opendataloader-bench](https://github.com/opendataloader-projec
 
 | Engine | Overall | Reading Order (NID) | Tables (TEDS) | Headings (MHS) | Speed (200 docs) |
 |---|---|---|---|---|---|
-| pdf-inspector | 0.78 | 0.87 | 0.59 | 0.57 | 4s |
+| pdf-inspector | 0.83 | 0.88 | 0.66 | 0.74 | 4s |
 | opendataloader | 0.84 | 0.91 | 0.49 | 0.74 | 11s |
 | pymupdf4llm | 0.73 | 0.89 | 0.40 | 0.41 | 18s |
 | markitdown | 0.58 | 0.88 | 0.00 | 0.00 | 8s |
 
-For context, engines that use OCR/ML (docling, marker, mineru) score 0.83-0.88 overall but take 2-180 minutes on the same corpus.
+For context, engines that use OCR/ML (docling, marker, mineru) score 0.83-0.88 overall but take 2-180 minutes on the same corpus — pdf-inspector reaches the low end of that range without any OCR, in 4 seconds.
 
-**Where we do well:** Speed (fastest of all engines), reading order, table detection vs other direct-text tools.
+**Where we do well:** Speed (fastest of all engines), the best table detection of any engine shown, and heading detection now on par with opendataloader. Overall lands within 0.01 of opendataloader at roughly 2.5× the speed.
 
-**Where we lag:** Heading detection trails opendataloader — many PDFs use bold text at body font size for headings, or headings that are only slightly larger than body text. Table detection trails OCR-based engines that can see visual table structure.
+**Where we lag:** Reading order still trails opendataloader slightly, and table structure trails OCR-based engines that can see visual layout.
 
 ## Quick start