[{"data":1,"prerenderedAt":379},["ShallowReactive",2],{"site-info":3,"work-receipt-invoice-ocr":99},{"id":4,"company":5,"extension":16,"meta":17,"services":18,"social":96,"stem":97,"__hash__":98},"site\u002Fsite.yml",{"name":6,"nameEn":7,"email":8,"address":9,"addressEn":10,"lineUrl":11,"lineId":12,"duns":13,"youtube":14,"youtubeId":15},"銡策數位有限公司","Jecision Digital Co., Ltd.","contact@jecision.com","231 新北市新店區北新路二段 262 號 3 樓之 3","3F.-3, No. 262, Sec. 2, Beixin Rd., Xindian Dist., New Taipei City 231, Taiwan","https:\u002F\u002Fline.me\u002FR\u002Fti\u002Fp\u002F@374qnumi","@374qnumi","963905705","https:\u002F\u002Fwww.youtube.com\u002Fshorts\u002F6UE0WTmyjsg","6UE0WTmyjsg","yml",{},[19,43,69],{"key":20,"zh":21,"en":32},"audit",{"title":22,"tagline":23,"summary":24,"points":25,"deliverable":29,"detail":30,"duration":31},"企業 AI 導入健檢","先看清楚，再花錢","60 分鐘線上訪談，盤點最耗時、最依賴人力的營運環節；我們會告訴你哪些工作現在就能交給 AI、哪些還不成熟。",[26,27,28],"現況盤點：梳理日常營運中最耗時、最依賴人力的環節","痛點診斷：判斷哪些工作現在就能交給 AI，哪些建議先保留人力","可行性評估：依你的業務特性與預算，篩選適合的模型與工具","《企業 AI 導入建議報告書》（PDF）","含 1–2 個「開發成本最低、成效最明顯」的優先項目、技術棧建議、預期節省的人力時間與下一步","訪談 60 分鐘，報告書 3 個工作天內交付",{"title":33,"tagline":34,"summary":35,"points":36,"deliverable":40,"detail":41,"duration":42},"AI Readiness Audit","Look before you spend","A 60-minute remote audit of the workflows that eat the most time and people-power — and an honest answer on what AI can take over today, and what it cannot.",[37,38,39],"Audit the daily workflows that consume the most time and headcount","Triage which tasks AI can own today, and which should stay human","Shortlist the models and tools that fit your budget and constraints","AI Adoption Recommendation Report (PDF)","One or two highest-ROI entry points, a recommended tech stack, expected hours saved, and the concrete next step","60-minute call, report delivered within 3 business days",{"key":44,"zh":45,"en":56},"prompt",{"title":46,"tagline":47,"summary":48,"points":49,"deliverable":53,"detail":54,"duration":55},"提示詞工程","讓 AI 穩定產出，而不是碰運氣","針對你的實際任務設計提示詞與多步驟工作流，並做極限測試，確保換了資料、換了人操作也不會走鐘。",[50,51,52],"客製提示詞：ChatGPT、Claude、Gemini、Midjourney 等主流工具的專屬模板","除錯與優化：修掉幻覺、格式跑掉、時好時壞的問題","工作流建置：把複雜任務拆成多步驟提示詞與可重複的 SOP","提示詞文件或設定檔","附使用說明與範例，並提供 1 對 1 教學，確保你與團隊能自己維護","依任務複雜度報價，通常 3–7 個工作天",{"title":57,"tagline":58,"summary":59,"points":60,"deliverable":66,"detail":67,"duration":68},"Prompt Engineering","Make AI produce reliably, not by luck","Prompt sets and multi-step workflows designed for your actual tasks, stress-tested so they keep working when the data or the operator changes.",[61,62,64],"Custom prompts for ChatGPT, Claude, Gemini, Midjourney and friends",{"Debugging and tuning":63},"kill hallucinations, broken formats and flaky output",{"Workflow design":65},"break complex work into repeatable multi-step SOPs","Prompt library or exportable config","Ships with usage notes and examples, plus a 1:1 walkthrough so your team can maintain it","Quoted by complexity, typically 3–7 business days",{"key":70,"zh":71,"en":82},"poc",{"title":72,"tagline":73,"summary":74,"points":75,"deliverable":79,"detail":80,"duration":81},"極速 PoC","五天，看到能跑的東西","不談架構藍圖，直接做一個能動的雛形。五個工作天內交付線上可操作的智能體，用你真實的資料驗證。",[76,77,78],"Day 1：15 分鐘對焦，鎖定單一痛點與資料盤點","Day 2–4：模型串接、邏輯設計與雛形開發","Day 5：Demo 展示與 AI 擴展建議書","可線上操作的智能體雛形","交付範圍為概念驗證與展示，不含後續維運或大規模伺服器部署；要進階成完整系統會另行報價","5 個工作天",{"title":83,"tagline":84,"summary":85,"points":86,"deliverable":93,"detail":94,"duration":95},"Rapid PoC","Five days to something that runs","No architecture decks — a working prototype built in five business days, validated against your real data.",[87,89,91],{"Day 1":88},"15-minute scoping call, one pain point, data inventory",{"Day 2–4":90},"model integration, logic design, prototype build",{"Day 5":92},"live demo plus an AI expansion recommendation","A runnable online agent prototype","Proof of concept and demo scope only; ongoing operations and large-scale deployment are quoted separately","5 business days",[],"site","XTwARfVAOcrXNiKehsM6V6cdkd2HXy4d5yZVrSWrI3Q",{"zh":100,"en":242},{"id":101,"title":102,"body":103,"client":213,"cover":214,"coverAlt":215,"description":216,"draft":217,"extension":218,"industry":219,"lang":220,"meta":221,"navigation":222,"order":201,"path":223,"results":224,"seo":234,"services":235,"slug":238,"stem":239,"translationKey":240,"year":240,"__hash__":241},"work\u002Fwork\u002Freceipt-invoice-ocr.md","台灣的收據與發票，直接讀進資料庫",{"type":104,"value":105,"toc":199},"minimark",[106,110,114,117,125,128,133,136,140,143,150,154,157,160,164,167,170,177,183,186],[107,108,109],"h2",{"id":109},"問題",[111,112,113],"p",{},"台灣的收據不是一種東西。",[111,115,116],{},"電子發票有 QR code 與固定欄位，傳統三聯式發票是印刷格線，手寫收據則是另一個世界——字跡、印章、螢光筆、折痕、拍照角度，每一項都會讓通用 OCR 出錯。",[111,118,119,120,124],{},"出錯的方式還很討厭：",[121,122,123],"strong",{},"它不會說自己錯了","。品項少抓一欄、稅額多一個 0，看起來都很正常，等到對帳才發現。",[107,126,127],{"id":127},"做法",[129,130,132],"h3",{"id":131},"_1-先把圖修好再談辨識","1. 先把圖修好，再談辨識",[111,134,135],{},"影像前處理比模型更關鍵。歪掉的、逆光的、皺掉的單據，先做裁切、透視校正、對比增強，讓模型看到的是「一張正面的、乾淨的紙」。這一步做好，後面的辨識率差距非常明顯。",[129,137,139],{"id":138},"_2-地端-qwen-做欄位抽取","2. 地端 Qwen 做欄位抽取",[111,141,142],{},"模型直接輸出結構化欄位：日期、店家、統編、品項、數量、單價、稅額、總額。",[111,144,145,146,149],{},"跑在地端不只是為了省 API 費用，而是",[121,147,148],{},"收據裡有客戶的消費明細","，那是不能隨便送出去的資料。",[129,151,153],{"id":152},"_3-寫回資料庫而不是丟一個-excel","3. 寫回資料庫，而不是丟一個 Excel",[111,155,156],{},"辨識完不是產出一份表格讓人工再貼一次。我們把輸出接上一個自動寫入的流程，直接寫進資料庫的對應欄位——會計打開系統，資料已經在裡面。",[111,158,159],{},"這個流程的技術形式，是我們替客戶的 AI agent 寫的一個「技能」（skill）：它收到結構化資料後，先驗證統編與日期，再寫進對應的資料表。對使用者來說，它就是「不用再手動輸入」而已。",[129,161,163],{"id":162},"_4-信心分數決定誰要覆核","4. 信心分數決定誰要覆核",[111,165,166],{},"低信心的欄位會被標記出來，會計只需要看那幾欄。高信心的直接放行。",[107,168,169],{"id":169},"結果",[111,171,172,173,176],{},"人工的角色從「輸入」變成「覆核」。這件事的差別不只是快，而是",[121,174,175],{},"錯誤會被看見","——低信心的欄位一定有人看過，而不是像以前一樣，錯了也要等到月底對帳才爆出來。",[178,179,180],"blockquote",{},[111,181,182],{},"我們沒有宣稱「全自動」。單據辨識在台灣的現實是：做得再好，還是會有一小部分需要人看。把它設計成「人看那一小部分」才是可維運的作法。",[107,184,185],{"id":185},"目前沒做的",[187,188,189,193,196],"ul",{},[190,191,192],"li",{},"沒有做手寫金額的專門模型，手寫比例高的單據仍以人工為主",[190,194,195],{},"沒有做多頁合併（一張收據拍成兩張照片）",[190,197,198],{},"準確率數字尚未做系統性量測，不敢寫在網站上",{"title":200,"searchDepth":201,"depth":201,"links":202},"",2,[203,204,211,212],{"id":109,"depth":201,"text":109},{"id":127,"depth":201,"text":127,"children":205},[206,208,209,210],{"id":131,"depth":207,"text":132},3,{"id":138,"depth":207,"text":139},{"id":152,"depth":207,"text":153},{"id":162,"depth":207,"text":163},{"id":169,"depth":201,"text":169},{"id":185,"depth":201,"text":185},"會計與記帳單位（客戶不公開）","\u002Fimages\u002Fwork\u002Freceipt-invoice-ocr.svg","收據與發票辨識示意圖","台灣的單據格式太雜——電子發票、傳統三聯式、手寫收據混在一起，通用 OCR 經常抓不到品項與稅額。我們在地端跑 Qwen 視覺模型辨識，再直接寫進資料庫，會計只需要覆核。",false,"md","財會作業","zh-TW",{},true,"\u002Fwork\u002Freceipt-invoice-ocr",[225,228,231],{"label":226,"value":227},"推論位置","地端",{"label":229,"value":230},"辨識模型","Qwen（視覺）",{"label":232,"value":233},"人工角色","覆核，不重新輸入",{"title":102,"description":216},[236,237],"automation","edge","receipt-invoice-ocr","work\u002Freceipt-invoice-ocr",null,"_A9zt0dilnQ4Cfaxq0CFeepL_s3k9i_DsUwocDv97v0",{"id":243,"title":244,"body":245,"client":357,"cover":358,"coverAlt":359,"description":360,"draft":217,"extension":218,"industry":361,"lang":362,"meta":363,"navigation":222,"order":201,"path":364,"results":365,"seo":375,"services":376,"slug":238,"stem":377,"translationKey":240,"year":240,"__hash__":378},"work\u002Fwork\u002Fen\u002Freceipt-invoice-ocr.md","Reading Taiwanese receipts straight into the database",{"type":104,"value":246,"toc":346},[247,251,254,257,264,268,272,275,279,282,289,293,296,304,308,311,315,326,331,335],[107,248,250],{"id":249},"the-problem","The problem",[111,252,253],{},"A receipt in Taiwan is not one thing.",[111,255,256],{},"E-invoices have QR codes and fixed fields. Traditional triplicate invoices are printed grids. Handwritten receipts are a different world entirely — handwriting, stamps, highlighter, creases, camera angle. Every one of those breaks generic OCR.",[111,258,259,260,263],{},"The failure mode is the nasty kind: ",[121,261,262],{},"it does not know it is wrong",". A missing line item or an extra zero in the tax field looks perfectly normal until month-end reconciliation.",[107,265,267],{"id":266},"what-we-built","What we built",[129,269,271],{"id":270},"_1-fix-the-image-before-talking-about-models","1. Fix the image before talking about models",[111,273,274],{},"Pre-processing matters more than the model. Deskew, correct perspective, normalise contrast, crop to the document — so the model sees a clean, upright page. The gap in recognition quality after this step is large.",[129,276,278],{"id":277},"_2-extract-fields-with-a-local-vision-model","2. Extract fields with a local vision model",[111,280,281],{},"The model outputs structured fields directly: date, vendor, tax ID, line items, quantity, unit price, tax, total.",[111,283,284,285,288],{},"Running on-premise is not about API cost. ",[121,286,287],{},"Receipts contain a customer's purchase details",", and that is not data you hand to a third party casually.",[129,290,292],{"id":291},"_3-write-to-the-database-not-to-a-spreadsheet","3. Write to the database, not to a spreadsheet",[111,294,295],{},"The output does not become an Excel file that someone re-types later. It goes through an automated write step that puts the values into the right fields — the accountant opens the system and the data is already there.",[111,297,298,299,303],{},"Under the hood, that step is a capability (a ",[300,301,302],"em",{},"skill",") we wrote for the client's AI agent: it validates the tax ID and date, then writes into the matching tables. From the user's side it is simply \"no more manual entry\".",[129,305,307],{"id":306},"_4-confidence-decides-who-reviews-what","4. Confidence decides who reviews what",[111,309,310],{},"Low-confidence fields are flagged. The accountant only looks at those; the rest flow through.",[107,312,314],{"id":313},"the-outcome","The outcome",[111,316,317,318,321,322,325],{},"The human role changes from ",[121,319,320],{},"data entry"," to ",[121,323,324],{},"review",". That is not only faster — it means errors are actually seen, instead of surfacing at reconciliation.",[178,327,328],{},[111,329,330],{},"We do not claim \"fully automatic\". The reality of receipt recognition in Taiwan is that a small fraction will always need a human. Designing for \"the human reviews that fraction\" is what makes it operable.",[107,332,334],{"id":333},"what-is-not-done","What is not done",[187,336,337,340,343],{},[190,338,339],{},"No dedicated model for handwritten amounts; receipts with heavy handwriting remain manual",[190,341,342],{},"No multi-page stitching (one receipt photographed across two images)",[190,344,345],{},"Accuracy numbers have not been measured systematically, so we do not publish them",{"title":200,"searchDepth":201,"depth":201,"links":347},[348,349,355,356],{"id":249,"depth":201,"text":250},{"id":266,"depth":201,"text":267,"children":350},[351,352,353,354],{"id":270,"depth":207,"text":271},{"id":277,"depth":207,"text":278},{"id":291,"depth":207,"text":292},{"id":306,"depth":207,"text":307},{"id":313,"depth":201,"text":314},{"id":333,"depth":201,"text":334},"Accounting firm (client not disclosed)","\u002Fimages\u002Fwork\u002Freceipt-invoice-ocr-en.svg","Receipt and invoice recognition","Receipts in Taiwan come in too many shapes for generic OCR to handle. We run a vision model on-premise, extract structured fields, and write them straight into the database.","Accounting operations","en",{},"\u002Fwork\u002Fen\u002Freceipt-invoice-ocr",[366,369,372],{"label":367,"value":368},"Inference","On-premise",{"label":370,"value":371},"Model","Qwen (vision)",{"label":373,"value":374},"Human role","Review, not retype",{"title":244,"description":360},[236,237],"work\u002Fen\u002Freceipt-invoice-ocr","TsaAOnPGgZchfoD1ssogv4-xrz96YyKqdO4NspGRSug",1792775653364]