PaddleOCR in Practice: Extracting Hong Kong Stock Annual Report Financial Data in 83 Seconds

Core proposition: What is the most time-consuming part of Hong Kong stock M&A due diligence? It is not analysis, but manually copying financial figures from annual report PDFs. Can PaddleOCR automate this process?
Data: 81K+ Stars · 102 text regions · 96.5% average confidence · 83-second three-statement extraction · 0 errors in key figures
Methodology: Clone → venv → Transformers engine → PP-OCRv5 test → PP-StructureV3 KMeans Bug analysis → custom-built DBSCAN Parser → end-to-end Pipeline → packaged as an AK-OCR skill

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Introduction: Annual Report Data Extraction, the Bottleneck in M&A Due Diligence

In pre-M&A due diligence for Hong Kong listed companies, we need to extract large amounts of financial data from the target company's annual report: income statement (P&L), balance sheet (BS), and cash flow statement (CF). The traditional approach is:

1. Download the annual report PDF from HKEXnews (hkexnews.hk)
2. Manually flip to the financial statement pages
3. Copy numbers line by line into Excel
4. Repeatedly cross-check to ensure accuracy

For a 200-page annual report, extracting just the three main statements takes 15-20 minutes, and it is prone to human transcription errors.

PaddleOCR, Baidu's open-source document AI engine (81K+ Stars, Apache 2.0), provides three core capabilities:

Module	Function	Scale
PP-OCRv5	Plain text recognition in 100+ languages	Runs on CPU
PaddleOCR-VL-1.6	0.9B-parameter VLM, OmniDocBench 96.33% SOTA	Multimodal document understanding
PP-StructureV3	Full-element structuring of tables, formulas, seals, and charts	Complete layout analysis

Our goal: use PaddleOCR to automate financial data extraction from Hong Kong stock annual reports.

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1. Environment Setup: Transformers Engine on macOS

PaddleOCR 3.x supports two inference engines: PaddlePaddle and Transformers (HuggingFace).

macOS (Apple Silicon) does not have an official PaddlePaddle wheel, but the Transformers engine is fully supported. Just run:

git clone https://github.com/PaddlePaddle/PaddleOCR.git
cd PaddleOCR
python3 -m venv venv && source venv/bin/activate
pip install "paddleocr[all]" transformers torch torchvision

After all dependencies are installed, the environment requires only about 1.5GB of disk space.

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II. PP-OCRv5 First Test: 00928 King International Investment FY2025 P&L Page

Test objective: page 90 of the FY2025 annual report of 00928.HK (King International Investment), the Consolidated Statement of Profit or Loss and Other Comprehensive Income.

from paddleocr import PaddleOCR

ocr = PaddleOCR(
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=False,
    engine="transformers",
lang="ch",  # Chinese + English mixed
)

result = ocr.predict("page_90.png")
for res in result:
res.save_to_json("output")  # Save structured results

Results

Metric	Value
Text region detection	102 items
Average confidence	96.5%
Low confidence (<85%)	5/102 (4.9%)
First inference time	~30 seconds (including model download)
Subsequent inference time	~27 seconds/page

All key financial figures are accurate:

Item	Original annual report	OCR extraction	Match
Revenue	40,765	40,765	✅
Cost of Sales	(28,033)	(28,033)	✅
Gross Profit	12,732	12,732	✅
Loss Before Tax	(44,906)	(44,906)	✅
Loss for Year	(47,454)	(47,454)	✅

The 5 low-confidence items mainly come from single characters or concatenated English letters:

• | (0.650), table separator
• \|I (0.328), should be 11 (note number)
• I,895 (0.904), should be 1,895

These errors can be handled entirely automatically with a regular expression correction table.

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3. PP-StructureV3 KMeans Bug: Technical Root Cause Analysis

PP-OCRv5 can only extract text and cannot understand table structure (row and column relationships). PP-StructureV3 is a pipeline designed specifically for table structuring.

However, when we ran PP-StructureV3 on the Transformers engine, we encountered a fatal error:

sklearn.utils._param_validation.InvalidParameterError:
The 'n_clusters' parameter of KMeans must be an int in the range [1, inf).
Got 0 instead.

Root Cause Tracing

The error occurred at line 478 of paddlex/inference/pipelines/table_recognition/pipeline_v2.py:

# Call Chain
cells_det_results_reprocessing()
  → combine_rectangles(cells + ocr_miss_boxes, html_pred_boxes_nums)
    → KMeans(n_clusters=N)  # N = html_pred_boxes_nums

Under the Transformers engine, the HTML table structure predicted by SLANeXt (table structure prediction model) had html_pred_boxes_nums = 0, meaning not a single cell was detected.

Passing N=0 to KMeans caused scikit-learn to directly throw InvalidParameterError.

Why Does This Happen?

The official PaddleOCR documentation clearly states: "Some models are currently still being supported," and the adaptation of the SLANeXt table structure model to the Transformers engine (PyTorch backend) has not yet been completed. It was originally designed for the PaddlePaddle engine. macOS does not have PaddlePaddle, so this path is temporarily unavailable.

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4. Building Our Own Table Parser: DBSCAN Row and Column Clustering

Since PP-StructureV3 is unavailable, we need an alternative. The core idea is: use the bounding box coordinates returned by OCR and apply DBSCAN clustering to reconstruct row and column relationships.

Algorithm Design

Step 1: Y-axis DBSCAN clustering → "rows"
   → All text boxes with similar Y-coordinate midpoints are grouped into the same row
Step 2: Sort along the X-axis within each row → "Columns"
   → Sort by X coordinate within the same row

Step 3: Chinese-English Bilingual Merging
   → Feature of Hong Kong stock annual reports: a single cell contains two lines in Chinese and English
   → Merge non-numeric pairs with X-axis distance <120px

Step 4: Column Grid Detection
   → Global X-axis DBSCAN establishes a unified column boundary

Full source code (table_parser_v2.py):

import numpy as np
from sklearn.cluster import DBSCAN
from collections import defaultdict

def parse_financial_table(ocr_json, eps_y=14, eps_x=60):
    texts = [fix_ocr_text(t) for t in ocr_json['rec_texts']]
    boxes = ocr_json['rec_boxes']
    
    # Step 1: Y-axis clustering → rows
    y_centers = np.array([(b[1]+b[3])/2 for b in boxes]).reshape(-1,1)
    labels = DBSCAN(eps=eps_y, min_samples=1).fit(y_centers).labels_
    
    rows = defaultdict(list)
    for i, label in enumerate(labels):
        rows[label].append({
            'text': texts[i], 'score': scores[i],
            'x_center': (boxes[i][0]+boxes[i][2])/2,
            'y_center': (boxes[i][1]+boxes[i][3])/2,
        })
    
    # Step 2: Sort rows by Y, sort items within row by X
    sorted_rows = []
    for label, items in rows.items():
        items_sorted = sorted(items, key=lambda it: it['x_center'])
        items_sorted = merge_bilingual(items_sorted, eps_x=120)
        sorted_rows.append({
            'row_y': np.mean([it['y_center'] for it in items_sorted]),
            'cols': items_sorted,
        })
    sorted_rows.sort(key=lambda r: r['row_y'])
    
    # Step 3: Global column grid
    all_x = np.array([it['x_center'] for r in sorted_rows 
                      for it in r['cols']]).reshape(-1,1)
    x_labels = DBSCAN(eps=eps_x, min_samples=2).fit(all_x).labels_
    
    # Step 4: Assign each item to its column
    # ... (full code in repo)

Results

00928.HK annual report P&L page: 34 rows × 6 columns, fully matches the original table structure.

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5. AK-OCR Pipeline: End-to-End Automation

Encapsulate the above components into a complete Pipeline:

PDF → PyMuPDF page splitting (200dpi) → PP-OCRv5 → Table Parser → JSON/MD output

End-to-End Test Results (00928 18M FY2025 Annual Report)

Stage	Time
Phase 1: PDF → Images (3 pages)	0.8s
Phase 2: PP-OCRv5 Text Recognition	123.2s
Phase 3: Table Parser Structuring	~1s
Phase 4: JSON + MD Output	<1s
Total	~125s

Statement	Text Regions	Average Confidence	Structured Output
P&L (p.91)	102	96.5%	34 rows × 6 columns
BS (p.93)	100	98.3%	32 rows × 6 columns
CF (p.96)	114	96.5%	39 rows × 5 columns

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6. OCR Correction Table: Automatic Correction of Common Errors

Common misrecognition patterns of PP-OCRv5 for mixed Chinese and English documents:

OCR_FIXES = [
    (r'\b3I\b', '31'),           # "3I March" → "31 March"
    (r'\bI,(\d)', r'1,\1'),      # "I,895" → "1,895"
    (r'\(2I\)', '(21)'),         # "(2I)" → "(21)"
    (r'\b\|I\b', '11'),          # "|I" → "11"
    (r'15,1\|4', '15,114'),      # "15,1|4" → "15,114"
    (r'diferencesarising', 'differences arising'),
    (r'subsequentl y\b', 'subsequently'),
]

These correction tables can be continuously expanded as new annual report formats emerge.

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7. Conclusion and Next Steps

Core Value

Metric	Manual Extraction	AK-OCR Pipeline
P&L extraction from one annual report	~15 min	~30 sec
Accuracy	Risk of human error	96.5%+
Reusability	Redone each time	One-click batch
Cost	Manual labor	0 (local CPU)

Known Limitations

1. PP-StructureV3 is unavailable: must wait for an official fix for Transformers engine compatibility, or switch to the Docker PaddlePaddle version
2. OCR character misrecognition: digits 1↔I, touching characters; can be continuously improved with a correction table
3. Page number offset: annual report page numbers ≠ PDF index (difference of ~1-2 pages); automatic TOC scanning is built in

Source Code Locations

• Pipeline: ~/workspace/PaddleOCR/ak_ocr_pipeline.py
• Table Parser: ~/workspace/PaddleOCR/table_parser_v2.py
• Skill documentation: ~/workspace/skills/ak-ocr/SKILL.md

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This article is based on real-world test data from an actual Hong Kong listed company annual report (00928.HK Emperor International Investment FY2025). All OCR results can be reproduced locally.