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feat(ocr): T0a/T0/T1 — golden harness(CTC blank=0 도출) + deps(ort rc.9) + dict/NOTICE

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T0a: onnxruntime 직접 골든 하네스 → CTC blank/dict 매핑 경험 확정(gt CER 0.000).
T0: 모델 번들 dict+NOTICE(.onnx 는 T12 LFS 결정까지 워크트리 보관).
T1: ort(download-binaries)+imageproc 추가, cargo tree ort rc.9 단일 확인.
This commit is contained in:
altair823
2026-06-04 07:43:53 +00:00
parent 75a543ff69
commit 8f8d3a4100
7 changed files with 12667 additions and 0 deletions
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14
crates/kebab-parse-image/Cargo.toml
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@@ -35,6 +35,20 @@ kamadak-exif = "0.6"
# transitive tokio runtime is brought in once.
reqwest = { version = "0.12", default-features = false, features = ["blocking", "json", "rustls-tls"] }
base64 = { workspace = true }
thiserror = { workspace = true }
# paddle-onnx OCR engine (PP-OCRv5, in-process). We reuse the workspace ort
# pin (=2.0.0-rc.9) so the ONNX Runtime native lib stays single-versioned with
# fastembed / kebab-nli (oar-ocr is intentionally NOT a dep — it would pull
# ort rc.12 + ndarray 0.17, splitting the native `links` and threatening the
# embedding stack). `download-binaries` extends the pin the same way
# `kebab-nli/Cargo.toml:23` does: this crate isn't in fastembed's build graph,
# so a standalone `cargo test -p kebab-parse-image` needs it to link onnxruntime.
ort = { workspace = true, features = ["ndarray", "download-binaries"] }
ndarray = { workspace = true }
# imageproc: connected-components / contours for DBNet det post-processing.
# min-area rotated-rect (rotating calipers) and polygon unclip are implemented
# in pure Rust (clipper2 is C++ FFI — would break the single-binary guarantee).
imageproc = "0.25"
[dev-dependencies]
tempfile = { workspace = true }

33
crates/kebab-parse-image/assets/paddleocr-onnx/NOTICE Normal file
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@@ -0,0 +1,33 @@
PP-OCRv5 mobile ONNX models bundled with kebab (paddle-onnx OCR engine)
=======================================================================
These model weights and the recognition dictionary are derived from
PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR), licensed under the
Apache License, Version 2.0.
Copyright (c) PaddlePaddle Authors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use these files except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Files
-----
ppocrv5_mobile_det.onnx PP-OCRv5_mobile detection model (DBNet)
korean_ppocrv5_mobile_rec.onnx korean_PP-OCRv5_mobile recognition model (CTC)
korean_dict.txt recognition dictionary (11,945 chars: KR + Latin + digits + symbols)
These were converted from the official PaddlePaddle inference models to ONNX
via paddle2onnx for in-process execution with onnxruntime (`ort`). No model
architecture or weights were modified; only the serialization format changed.
The recognition CTC class layout (empirically confirmed, see
tests/golden/ctc_rec_golden.json):
index 0 = CTC blank
index 1..11945 = korean_dict.txt line N -> class N (dict[N-1])
index 11946 = space ' '
total classes = 11947 (= 11945 dict + blank + space)
If any post-processing source (min-area-rect / polygon unclip) is later
ported verbatim from oar-ocr (Apache-2.0), record the per-file provenance
here as required by the Apache-2.0 attribution clause.

11945
crates/kebab-parse-image/assets/paddleocr-onnx/korean_dict.txt Normal file
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File diff suppressed because it is too large Load Diff

516
crates/kebab-parse-image/tests/golden/ctc_rec_golden.json Normal file
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@@ -0,0 +1,516 @@
{
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"rec_norm": "RGB, /255 then (x-0.5)/0.5 => [-1,1], height=48 keep-aspect pad",
"det_norm": "RGB, ImageNet mean/std *255 then /std, NCHW",
"rec_cases": [
{
"text": "RAG 시스템 검색 결과",
"decoded": "RAG시스템 검색 결과",
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]
},
{
"text": "Embedding vector 0123",
"decoded": "Embedding vector 0123",
"cer": 0.0,
"cer_nospace": 0.0,
"mapping_ok": true,
"T": 41,
"C": 11947,
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]
},
{
"text": "한글 OCR 정확도 테스트",
"decoded": "한글 OCR 정확도 테스트",
"cer": 0.0,
"cer_nospace": 0.0,
"mapping_ok": true,
"T": 41,
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}
],
"det_cases": [
{
"fixture": "clean_paragraph.png",
"orig_hw": [
192,
900
],
"det_input_hw": [
192,
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],
"prob_shape": [
192,
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],
"prob_max": 1.0,
"prob_mean": 0.1139,
"positives_at_0.3": 19682,
"positive_frac": 0.1144,
"box_count": 3,
"postproc": "thresh=0.3 -> findContours -> minAreaRect -> unclip(ratio=1.5, area*r/peri); box_thresh=0.5 mean-prob filter; coords scaled back to orig hw"
}
],
"blank_index_confirmed_by_gt": true
}

78
crates/kebab-parse-image/tests/golden/det_boxes_clean_paragraph.json Normal file
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@@ -0,0 +1,78 @@
{
"fixture": "clean_paragraph.png",
"orig_hw": [
192,
900
],
"det_input_hw": [
192,
896
],
"thresh": 0.3,
"unclip_ratio": 1.5,
"boxes": [
{
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[
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}
]
}