5 articles tagged “benchmarks”.
We set out to lead every Armenian OCR engine we can measure on every register. Before training anything, we found that most of the gap we were chasing was in our own metric. The rest came down to reading order, a word list and a second fine-tune: v6 now leads both means and most registers outright
We measured two compact vision-language models as candidate local backends. One is in. The other produced the best score any engine has recorded on our hardest fixture, and it did it by inventing words that were never on the page
Three weeks ago we published a cross-engine comparison with one row missing, because the model had gone and we would not guess at it. We have now downloaded it and run it over every register. It takes the mean off us, and it takes every register the previous table had given to Calfa's Tesseract model
Until now, every cross-engine number we had published for Armenian came from ten encyclopedia pages. We have now run the other engines across all eight of our evaluation registers. Our model still leads on average, but it loses one register in three, and two things we thought we knew about where the gap lives turned out to be wrong
Ten human-proofread encyclopedia pages, ten OCR configurations measured against them, and the first honest numbers from a recogniser we are training ourselves — with the code, data and weights all published in the open