33b38aacc7cc4b4e928d954200ca9d027dd872f2
4 Commits
| Author | SHA1 | Message | Date | |
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devin-ai-integration[bot]
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33b38aacc7 |
Phase 3: PP-Structure table extraction + personnel column mapper (#2)
* Phase 3: PP-Structure table extraction + personnel column mapper Adds the personnel-table stage of the pipeline. PaddleOCR's PP-Structure recognizes table regions and emits HTML, which we parse into a 2D cell grid. A separate column mapper detects the header row, classifies each column to a canonical PersonnelEntry field via a synonym dictionary, and walks the data rows. Variant handling: - Different satuan use different column orders and header phrasing. Supported synonyms for each canonical field are listed in pipeline/extract/personnel.py (Pangkat / NRP / Pangkat-NRP combo / Nama / Jabatan dalam Dinas / Jabatan dalam Sprint / Keterangan). - A merged 'PANGKAT NRP' or 'PANGKAT NRP NAMA' cell is split using the 8-digit NRP regex (with look-arounds so glued forms like 'BRIPKA98050505' work) and the master pangkat lookup. - Unknown ranks are kept verbatim so the validation layer can flag them as UNKNOWN_PANGKAT for HITL review. - Rows without nrp AND nama are dropped (separators / merged cells). New module pipeline/table.py: - DetectedTable dataclass (cells + html). - parse_table_html: tag/entity-tolerant HTML -> 2D grid. - extract_tables_from_pp_result: filter PP-Structure regions to type=table. - run_table_extraction: top-level entrypoint with lazy-init singleton for the heavy PP-Structure engine. Orchestrator now invokes table extraction (gated by TABLES_ENABLED) on every preprocessed page and merges the discovered personnel into the ExtractionResult. Failures are caught and logged so a flaky table recognizer never blocks header extraction. Tests: 38 new unit tests covering HTML parsing, region filtering, header classification, column mapping (split, combined, glued cells), and end-to-end personnel extraction. Total 108 tests, all green. PaddleOCR / PP-Structure remain optional - no test imports them. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: fix header misclassification for combined Pangkat/NRP/Nama columns Devin Review caught two related bugs in personnel column mapping: 1. _classify_header_cell iterated _HEADER_SYNONYMS in insertion order when falling back to substring matching. The dict listed shorter keywords first ('pangkat' before 'pangkat / nrp'), so a header like 'Pangkat / NRP / Nama' classified as plain 'pangkat'. map_row then tried to normalize the whole '"AKP 87010101 Budi Santoso"' cell as a rank, normalize_pangkat returned None, and the row failed the nrp-or-nama gate at the bottom of map_row -- silently dropping every personnel row in tables using this layout. 2. _split_pangkat_nrp_nama existed and was unit-tested but was never wired up in map_row, so even if classification had worked, the three-way split would not have run. The module docstring claimed the split was supported. Fix: - Iterate the synonym table sorted by keyword length descending in the substring-match fallback so the most specific synonym wins. - Add 'pangkat_nrp_nama' synonym entries for typical separators (' / ', '/', whitespace, comma). - Wire 'pangkat_nrp_nama' into map_row using the existing helper. - Update is_personnel_table so combined headers count as both an id signal and a name signal. Tests: 6 new asserts (parametrized), 1 regression test for triple- combined header end-to-end, 1 dedicated map_row test for the new column type. 114 tests total, all green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: handle multi-word Polri ranks in _split_pangkat_nrp_nama Devin Review caught: token-by-token is_valid_pangkat() check could not recognize multi-word ranks ('KOMBES POL', 'BRIGJEN POL', 'IRJEN POL', 'KOMJEN POL', 'JENDERAL POL'). For 'KOMBES POL 88123456 John Doe' the old code returned pangkat=None, nama='KOMBES POL John Doe', and the validator's UNKNOWN_PANGKAT flag never fired because pangkat was falsy. New behavior: greedy longest-prefix match. After stripping the NRP we try the leading 3-token, 2-token, 1-token slice against normalize_pangkat() and take the longest that maps to a canonical rank. Tokens after the matched rank become the nama. Unknown ranks fall through to pangkat=None and the rank text stays in the nama field, where downstream validation already flags the row. Tests: 5 new asserts (4 multi-word ranks + 1 unknown-rank fallback), 119 total green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> * Phase 3: don't count pangkat_nrp as a name signal in is_personnel_table Devin Review caught: a table with header ['No', 'Pangkat / NRP', 'Jabatan'] (no name column) was wrongly classified as a personnel table because pangkat_nrp was lumped into has_name. Such a table would produce PersonnelEntry rows with nama=None passing the nrp-or- nama gate, polluting the personel[] output with id-only fragments. Split the combined-cell set into combined_id (counts toward has_id) and combined_name (counts toward has_name). Only pangkat_nrp_nama, which actually embeds a name, qualifies for has_name. pangkat_nrp remains an id-only signal. Tests: 3 new asserts (rejects id-only, accepts pangkat_nrp + separate nama, accepts pangkat_nrp_nama). 122 total green. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> |
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Adrian Kuman Firmansah
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812ea7e030 |
Merge pull request #1 from Adriankf59/devin/1777129396-phase-2-document-detection
Adds OpenCV-based phone-photo handling that runs before the standard preprocessing pipeline for IMAGE source kinds (PDF renders are flat by construction and skip this stage). Pipeline additions in src/ocr_sprint/pipeline/document_detect.py: - _find_document_quad: Canny + dilate + contour search, picks the largest convex 4-point polygon above a configurable area threshold; fails gracefully and returns None when no usable quad is found. - _four_point_warp: orders corners (TL/TR/BR/BL via sum/diff trick) and runs cv2.getPerspectiveTransform + warpPerspective. - _remove_shadow: per-channel background-division (dilate + median blur + 255 - absdiff + normalize) for uneven phone-shot lighting. - detect_and_correct: top-level entrypoint with graceful fallback to the original image when detection fails. Wired into the synchronous orchestrator: only enabled for IMAGE sources, skipped for PDF. New settings: - preprocess_detect_document (default: true) - preprocess_remove_shadow (default: true) - preprocess_min_quad_area_fraction (default: 0.20) Tests: 9 new unit tests covering corner ordering, quad detection on synthetic skewed documents, perspective warp output sanity, shadow removal shape preservation, full-pipeline behavior, and graceful fallback when detection fails. 70 tests total, all green. ML-based dewarping (DewarpNet) and DocTR detector are deferred to a future phase per the roadmap; the existing API is structured so they can be added as alternative backends behind DocumentDetectConfig. Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> |
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Devin AI
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d0e1835cc1 |
Phase 2: document detection + perspective correction + shadow removal
Adds OpenCV-based phone-photo handling that runs before the standard preprocessing pipeline for IMAGE source kinds (PDF renders are flat by construction and skip this stage). Pipeline additions in src/ocr_sprint/pipeline/document_detect.py: - _find_document_quad: Canny + dilate + contour search, picks the largest convex 4-point polygon above a configurable area threshold; fails gracefully and returns None when no usable quad is found. - _four_point_warp: orders corners (TL/TR/BR/BL via sum/diff trick) and runs cv2.getPerspectiveTransform + warpPerspective. - _remove_shadow: per-channel background-division (dilate + median blur + 255 - absdiff + normalize) for uneven phone-shot lighting. - detect_and_correct: top-level entrypoint with graceful fallback to the original image when detection fails. Wired into the synchronous orchestrator: only enabled for IMAGE sources, skipped for PDF. New settings: - preprocess_detect_document (default: true) - preprocess_remove_shadow (default: true) - preprocess_min_quad_area_fraction (default: 0.20) Tests: 9 new unit tests covering corner ordering, quad detection on synthetic skewed documents, perspective warp output sanity, shadow removal shape preservation, full-pipeline behavior, and graceful fallback when detection fails. 70 tests total, all green. ML-based dewarping (DewarpNet) and DocTR detector are deferred to a future phase per the roadmap; the existing API is structured so they can be added as alternative backends behind DocumentDetectConfig. Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> |
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Devin AI
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ca0c0a0428 |
Phase 1 MVP: synchronous OCR + regex header extraction
Implements the foundation of the OCR Sprint service: - FastAPI app with /api/v1/health and /api/v1/documents (sync upload) - Pydantic v2 schemas for documents, extraction result, personnel - Pipeline: PDF/image ingest (PyMuPDF), preprocessing (resize, deskew, denoise, optional adaptive threshold), PaddleOCR wrapper, regex-based header extraction (nomor sprint, tanggal, satuan, perihal, dasar), signatory NRP, master-pangkat validation, confidence scoring + routing. - Tests: 61 unit tests covering regex rules, validators, preprocess, ingest, confidence, and API contract (PaddleOCR mocked). - Tooling: pyproject (setuptools), ruff, mypy strict, pytest, pre-commit, Dockerfile, docker-compose, Makefile. - Docs: README + docs/architecture.md (full hybrid stack rationale and 6-phase roadmap). Co-authored-by: adrian kuman firmansah <adriancuman@gmail.com> |