ocr_score — SYMBOL `ocr` op

• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.ocr_score(read_text, true_text) -> 'dict' (to call the implementation directly, import ocrclassic; ocrclassic.ocr_score(read_text, true_text) -> 'dict'; from the registry, opssymbol.get("ocr_score"))

• GPU: none (CPU reference implementation only)

Usage

Score a reading against the truth by Levenshtein alignment, counting misreads and rejects separately (table).

> The detailed description below is the original text — the summary and the headings are translated.

整列(置換・挿入・削除の費用 1)の後、整列した各対を数える:

• correct —— 同じ字

• rejected —— 真値の字に対して "?"(棄却: 安全側。人が見れば済む)

• substituted —— 真値と違う字(誤読: 危険側。検査では見逃しになる)

• inserted —— 真値に無い字が読まれた("?" の挿入も含む)

• deleted —— 真値の字が読まれなかった

誤読と棄却は決して 1 つの率に畳まない。率は真値の字数で割る。Misreads and rejects are never folded into one rate. Rates are per true character.

Detailed usage guide

• symbol_reading family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_symbol_lot_ocr — py -3.11 examples/poc_symbol_lot_ocr.py

Ops the type connects to (they accept `table` as input)

render_symbol · symbol_read_trials · symbol_read_pod · ocr_classify · ocr_read_line · ocr_read_trials

Same category (`ocr`)

ocr_font · ocr_render_text · ocr_segment_line · ocr_features · ocr_train · ocr_classify · ocr_read_line · ocr_read_trials

*Provenance: ocrclassic.py — SYMBOL operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

ocr_score — SYMBOL ocr op