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>
This commit is contained in:
devin-ai-integration[bot]
committed by
GitHub
GitHub
parent
812ea7e030
commit
33b38aacc7
@@ -1,11 +1,13 @@
|
||||
"""Synchronous pipeline orchestrator (Phase 1).
|
||||
"""Synchronous pipeline orchestrator (Phase 1-3).
|
||||
|
||||
Wires the individual stages together:
|
||||
|
||||
bytes → ingest → preprocess → OCR → regex extract → validate → score
|
||||
bytes -> ingest -> document_detect -> preprocess -> OCR
|
||||
-> [PP-Structure tables -> personnel mapper]
|
||||
-> regex header extract -> validate -> score
|
||||
|
||||
Phase 4 will replace this with a Celery task graph; Phase 3/5 will plug
|
||||
in PP-Structure for tables and an LLM extractor for variant fields.
|
||||
Phase 4 will replace this with a Celery task graph; Phase 5 will plug
|
||||
in an LLM extractor for variant fields.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -15,13 +17,16 @@ from dataclasses import dataclass
|
||||
from ocr_sprint.config import get_settings
|
||||
from ocr_sprint.pipeline.confidence import compute_confidence, route
|
||||
from ocr_sprint.pipeline.document_detect import DocumentDetectConfig, detect_and_correct
|
||||
from ocr_sprint.pipeline.extract.personnel import extract_personnel
|
||||
from ocr_sprint.pipeline.extract.regex_rules import extract_header, find_signatory
|
||||
from ocr_sprint.pipeline.extract.validators import validate_extraction
|
||||
from ocr_sprint.pipeline.ingest import detect_source_kind, ingest
|
||||
from ocr_sprint.pipeline.ingest import NDArrayU8, detect_source_kind, ingest
|
||||
from ocr_sprint.pipeline.ocr import OCRPage, run_ocr
|
||||
from ocr_sprint.pipeline.preprocess import PreprocessConfig, preprocess
|
||||
from ocr_sprint.pipeline.table import DetectedTable, run_table_extraction
|
||||
from ocr_sprint.schemas.document import DocumentStatus, SourceKind
|
||||
from ocr_sprint.schemas.extraction import ExtractionResult, ReviewFlag
|
||||
from ocr_sprint.schemas.personnel import PersonnelEntry
|
||||
from ocr_sprint.utils.logging import get_logger
|
||||
|
||||
_logger = get_logger(__name__)
|
||||
@@ -66,9 +71,11 @@ def run_pipeline(content: bytes) -> PipelineOutput:
|
||||
)
|
||||
|
||||
ocr_pages: list[OCRPage] = []
|
||||
cleaned_pages: list[NDArrayU8] = []
|
||||
for page in pages:
|
||||
corrected = detect_and_correct(page.image, detect_cfg)
|
||||
cleaned = preprocess(corrected, pre_cfg)
|
||||
cleaned_pages.append(cleaned)
|
||||
ocr_pages.append(run_ocr(cleaned))
|
||||
|
||||
full_text = "\n".join(p.text for p in ocr_pages)
|
||||
@@ -77,13 +84,28 @@ def run_pipeline(content: bytes) -> PipelineOutput:
|
||||
header = extract_header(full_text)
|
||||
ttd = find_signatory(full_text)
|
||||
|
||||
personel: list[PersonnelEntry] = []
|
||||
if s.tables_enabled and cleaned_pages:
|
||||
all_tables: list[DetectedTable] = []
|
||||
for img in cleaned_pages:
|
||||
try:
|
||||
all_tables.extend(run_table_extraction(img))
|
||||
except Exception as exc: # pragma: no cover - defensive
|
||||
_logger.warning("pipeline.table_extraction_failed", error=str(exc))
|
||||
personel = extract_personnel(all_tables)
|
||||
_logger.info(
|
||||
"pipeline.tables",
|
||||
tables=len(all_tables),
|
||||
personel_rows=len(personel),
|
||||
)
|
||||
|
||||
initial_flags: list[ReviewFlag] = []
|
||||
if mean_ocr_conf < _OCR_CONFIDENCE_FLAG_THRESHOLD:
|
||||
initial_flags.append(ReviewFlag.LOW_OCR_CONFIDENCE)
|
||||
|
||||
result = ExtractionResult(
|
||||
header=header,
|
||||
personel=[], # Phase 3 will populate from PP-Structure
|
||||
personel=personel,
|
||||
untuk=[],
|
||||
ttd=ttd,
|
||||
raw_text=full_text,
|
||||
|
||||
Reference in New Issue
Block a user