PaddleOCR
百度macOS · Linux · Windows · Python
Open-source OCR — we use it to turn HK annual reports into analysable structured text
Good for
- Batch parsing annual reports and financial PDFs
- Traditional Chinese documents with mixed tables
- Self-hostable, offline-capable OCR pipelines
Not for
- Recognising one or two pages (a multimodal model is faster)
- Handwriting and badly skewed scans
We deliberately put “not for” on the same screen — a review that only lists strengths carries no information.
Pricing
Open source and free; cost is hardware plus your LLM API. This site has measured results on Traditional Chinese reports and tables.
Junze editorial rating
- Capability
- 44/5
- Ease of use
- 33/5
- Cost value
- 55/5
- Privacy control
- 55/5
Editorial opinion, not an aggregated user rating. Data last verified: 2026-09-29.
What you can finish with PaddleOCR
Each scenario is a full recipe: pain point → tool stack → steps → outcome.
- Turn a 300-Page HK Annual Report into Structured Financials
Digging numbers out of an annual report by hand takes two hours per company and still produces transcription errors.
First run working in 30 minutes6 steps - Build a DCF Valuation Model with AI, Including Sensitivity Analysis
A DCF model takes half a day to build, every assumption change forces a rebuild, and the sensitivity table is manual hell.
A sensitivity-enabled model in 1 hour7 steps - Run a Comparable-Company Analysis Across a Whole Sector
Collecting multiples for a dozen peers by hand gives inconsistent formats, and every refresh starts over.
One full sector in 90 minutes7 steps - Three-Statement Linkage and Cash-Flow Quality Check
Reported profit looks great while cash flow disagrees — nearly impossible to spot by hand across dozens of pages.
One company quality-checked in 1 hour8 steps - Contract Clause Extraction and Risk Flagging
Reading contracts page by page for key clauses is slow, and non-lawyers miss risk points.
A summary per contract in 10 minutes6 steps
Our articles about PaddleOCR
- PaddleOCR in Practice: Extracting Hong Kong Stock Annual Report Financial Data in 83 Seconds
A hands-on test of Baidu PaddleOCR, the open source document AI engine with 81K Stars. PP-OCRv5 with 96.5% accuracy extracted 102 text regions from a Hong Kong stock annual report P&L page, discovered a KMeans clustering bug (n_clusters=0) in the PP-StructureV3 Transformers engine, built a custom DBSCAN row and column clustering parser, and completed automatic structuring of three financial statements end to end in 83 seconds.
2026-06-08 · 17 min - 300 頁年報轉結構化 JSON：三表與分部數據
承接 PaddleOCR 實戰，把「讀出文字」推進到「可用的數據」：定位財報頁、處理跨頁表格、三表與分部資訊的欄位對應、單位統一、JSON schema 設計，以及用勾稽關係自動校對的完整管線與檢查表。
2026-09-30 · 22 min - 自建投研系統：從技能庫到驗證門控的完整設計
把前面各篇的方法收攏成一份完整藍圖：資料、技能、編排、驗證、輸出五層架構，技能庫的輸入輸出契約設計，從格式驗證到低信心棄權的五道門控，覆蓋率與準確率分開統計的原因，評測集建構七步法，上線後的維運機制，以及從單人工具到團隊系統的四階段演進路徑。
2026-09-30 · 24 min - 合約關鍵條款抽取與風險標記：逐頁閱讀的替代方案
合約又長又密，逐頁讀慢且容易漏。這篇用 PaddleOCR 處理掃描檔、Claude 做條款定位與轉錄、NotebookLM 對照標準範本，產出附原文位置的條款對照表並標記偏差；這不是法律意見，最終由合格法律人員確認。
2026-09-30 · 11 min - 機器怎麼讀懂財報：從 PDF 到結構化數據
財報分析自動化的第一道瓶頸是機器讀不進 PDF。本文拆解文字層抽取、OCR 與版面分析三條路徑，解釋財報表格為什麼特別難讀、OCR 之後為什麼必須人工校對，並給出結構化輸出（JSON schema）的設計概念。
2026-09-30 · 10 min - 三表聯動與現金流品質檢查：識別賬面利潤陷阱
利潤是觀點，現金是事實。本文拆解損益表、資產負債表、現金流量表的勾稽關係，給出現金轉換率、營運資本、資本化支出等檢查構面，附上一張「紅旗訊號→該查科目→驗證方法」速查表，並示範把這些檢查寫成可自動執行、可回溯的規則引擎。
2026-09-30 · 15 min - 可比公司分析自動化：一次跑完一整個板塊
可比公司分析的難度不在倍數算術，而在口徑：財政年度、幣別、一次性項目、EV 構成都可能讓兩家公司的數字互相污染。本文拆解同業挑選標準、六類口徑統一規則、倍數選擇的陷阱，並給出從資料抽取到異常值處理的五段自動化流水線設計與可執行程式碼。
2026-09-30 · 19 min
Learning tracks that include this tool
This page is an editorial summary, not an official page. Trademarks and product names belong to their respective owners. If anything here is wrong or out of date, tell us and we will correct it. Last verified: 2026-09-29.