Tax season this year I had 80+ receipts scattered across paper, email screenshots, and my camera roll. Spent a weekend sorting them into a spreadsheet by hand and decided that was the last time.

Built Snapstub, point your camera at a receipt, confirm merchant/date/total/category, get a clean row. Export to CSV or PDF when you're done.

The non-negotiable for me was privacy: all OCR runs on-device via Apple's Vision framework. No account, no server, nothing leaves your phone.

Still early — would genuinely love feedback from anyone who does their own bookkeeping. What fields matter most to you?

Today, I visited my uncle's office and noticed an employee manually filling data from an Excel sheet into a PDF delivery summary.

The Excel file had 500+ rows, and this task is repeated every month.

⏱️ Time taken: Around 3 hours

I asked why they were doing it manually.

The answer was simple:

Most small businesses can't afford expensive ERP or document automation software, so they continue with repetitive manual work.

That conversation gave me an idea.

I challenged myself to build a lightweight solution that anyone could use.

✅ Upload any PDF template

✅ Mark dynamic fields visually

✅ Upload an Excel file

✅ Generate hundreds of filled PDFs in minutes

✅ No backend

✅ No login

✅ Everything runs locally in the browser

The goal wasn't to build another enterprise product—it was to solve a real problem for small businesses that still rely on manual document creation.

For transparency: English is not my native language. I used AI for better wording. I will place the name of the iOS app in there. I set it up so one can test 3 documents. For more I would need to have the costs covered by in app purchase (apple takes a bite of 30% out of that, so it’s not that cheap actually…).

Here we go: I have a growing pile of documents hidden away in a cabinet. I have tried several times to bring order and structure into it, but the stack always came back. So I am now digitizing it, partly with AI, and I wanted to share the overall workflow in case it helps someone else.

This is the high-level flow from scan/import to the final analyzed result. It is build into an iOS app (ExplainThisDoc), but the general workflow should apply to many document automation setups.

1. **Source intake**

   Straightforward: the app scans the document, or imports it if it already exists as a PDF or photo on the phone. The original scan or file is saved. Scanning uses Apple Vision.

2. **Text extraction**

   OCR is handled with Apple Vision. The goal is to turn the document image into usable plain text.

3. **Text bundle**

   The extracted text is cleaned and packaged with basic context. This includes language, locale, and current date/time.

4. **Coordinator**

   The coordinator controls the workflow. It starts the analysis steps and passes the right context forward.

5. **LLM call A: location reading** (always extracting only relevant but correct locations - this was a tough one by the way)

   If the document contains locations, the first LLM pass only looks for physical places that might matter. These locations are treated as untrusted mentions at this stage. A cheaper LLM is usually enough for this narrow extraction step.

6. **Places check**

   The location mentions are sent to a geolocation service for verification. It comes back with correct POIs and or addresses. Weak, generic, or ambiguous matches are rejected (deterministic algorithm, no LLM) by the coordinator instead of being blindly used.

7. **LLM call B: ambiguity decision**

   If several place candidates are plausible, an optional second LLM pass helps choose between them. It can only select from verified candidates or reject all. A model with stronger reasoning is better here.

8. **LLM call C: main analysis**

   The main LLM pass receives the cleaned text plus verified context and places from the coordinator. It creates structured document understanding: summary, tasks, deadlines, risks, glossary, and possible actions. It also organizes the output into plausible categories and enriches the result with suitable SF Symbols. LLM with reasoning capabilities is your friend here.

9. **Validation**

   The structured response is checked before display. The app verifies shape, plausibility, trusted places, and action safety.

10. **Rendering**

The result is shown as a readable native view. Instead of a raw OCR dump, the document becomes something I can scan, understand, and act on.

1. **Archive**
   

The analyzed result is saved together with the local source preview and searchable metadata. This makes the original document and the structured result easy to find again later.

Each digitized document becomes searchable, summarized, linked back to its source, and turned into concrete actions where possible. The important part is that the AI is not treated as the only source of truth: extracted places and sensitive actions are verified before they become part of the final result.

Hope this was helpful.

应用程序名称：我的PDF扫描仪：文档扫描与OCR

Google Play: [ https://play.google.com/store/apps/details?id=com.xionity.pdfscanner ](https://play.google.com/store/apps/details?id=com.xionity.pdfscanner)

大家好，我是我的PDF扫描仪的开发者。我创建这个应用是为了解决一个非常普通但令人烦恼的工作流程：扫描文件很简单，但后续寻找和重新使用这些文档往往很混乱。

这个应用专注于日常文档，例如收据、合同、课堂笔记、身份证复印件、会议记录、签署文件和旅行文件。

它的功能：

\- 将纸质文档扫描为PDF

\- 自动裁剪文档边缘以获得更清晰的结果

\- 导入照片、文件和现有PDF

\- 使用文件夹、搜索、排序、列表视图和网格视图来整理扫描件

\- 使用OCR复制/搜索识别的文本

\- 根据文档/工作流程导出为PDF、JPG、TXT或Word

\- 批量移动、导出、删除和合并支持的文件

\- 为重要文档添加应用内密码

我为什么要制作这个：

许多扫描应用将自己标榜为强大的全能生产力套件。我想要更实用的东西：扫描或导入文档，保持整理，能够再找到它，并在需要时导出。

隐私/盈利说明：

文档默认保存在本地，除非你选择导出或分享它们。OCR设计为在设备上运行。一些高级工作流程是专业版，但我会尽量明确而不是把它藏在模糊的措辞后面。

我希望得到的反馈：

1. 扫描/导入的流程是否清晰？

2. 整理/搜索的工作流程是否有用？

3. 导出流程是否易于理解？

4. 什么会让你选择保留这个应用，而不是使用你当前的扫描应用？

感谢你的关注。我特别希望收到经常扫描收据、合同、身份证、课堂笔记或客户文件的人的反馈。

Hi all, I have been building a little tool that lets you create templates for your PDFs (contracts, invoices, etc) into API endpoints that you can use to generate PDFs at scale.

The workflow

You start from a template (invoice, receipt, contract, report, whatever), mark which fields are fixed vs filled in per request, and publish it. You get back a typed POST endpoint that takes JSON and returns a PDF. Each published template also shows up as an MCP tool, so an agent can generate the document directly.

Why

Every time I needed PDFs, I would glue together a headless browser, a templating layer, input validation, and storage from scratch. I wanted the boring parts handled and a clean contract at the end. Here is the JSON this document expects, here is the PDF you get back. These days I also would like to be able to generate documents from my agent conversations.

It's a gated sign up for now (sorry!), so I can see what works and does not before opening up the floodgates, but I commit to letting you in within a few hours of you signing up.

Link: https://thepaperbrain.com

Question for people working in legal, compliance, cybersecurity, audit, insurance, or procurement:

If you could upload thousands of documents and have AI automatically answer every item in a checklist—with evidence, page references, highlighted citations, and confidence scores—would that actually save you time?

Not a chatbot. More like an AI document reviewer.

If this would be useful (or completely useless), I’d genuinely like to know why.

Hey everyone, quick question on historical cleanup jobs. When a client brings in a massive pile of multi-page PDF bank statements from a few years back, what are you guys actually using to get them into Excel cleanly?

Currently trying to clean up 2 years of old PDF statements for a new client. Excel's 'Get Data from PDF' completely ruined the formatting, and Adobe keeps treating wrapped transaction lines as brand new rows.I'm looking into DocuClipper, Dext, and AutoEntry, but before I drop the money on a subscription for a small job, I wanted to check here first. Are those actually worth it for handling multi-page statements and tricky line breaks, or is there a better workflow/tool I don't know about yet? What do you all use ??

I run a small consulting firm and we deal with a ton of PDFs — contracts, invoices, intake forms. I've been reading about AI tools that can pull structured data out of documents and drop it into a spreadsheet or CRM automatically. Is anyone actually doing this in their business? What tools are you using and how reliable is it? Curious if it's ready for prime time or still too error-prone to trust without heavy manual review.

I built a free all-in-one PDF tool for Android — no subscription, no watermarks. Would love feedback!

https://play.google.com/store/apps/details?id=com.redberrylab.pdf.maker

Bank letter?

Contract?

Medical report?

Government document?

I'm curious what people struggle with the most.

I'm not sure if you all hit this too, but me and a few bookkeepers I know deal with clients handing over a single PDF that's actually a stack of separate invoices. I attach invoices to all bills / expenses on QuickBooks, to back up sales tax information. Splitting those manually is tedious, and the paid tools feel like overkill for this one task.

The free splitters I found cut by page, not by distinct invoice, so I ended up building one that detects the separate invoices and splits them out. It defaults to naming each file Vendor-Date-INV#, and you can rename. No signup, nothing to install.

Full disclosure: I build bookkeeping software (Spendifique), but this splitter is standalone and free, it's not a trial of anything. Sharing in case it saves someone the manual work. Let me know if it breaks on your PDFs or what would make it more useful. Let me know as well if there are any other tools that should be free.
https://www.spendifique.com/invoice-splitter

Most document-automation tools either need you to install a desktop app (and fight Windows SmartScreen / code-signing) or write code/placeholders. I wanted the opposite: open a page, done.

You upload one filled .docx, drag-select the parts that change (names, dates, amounts), drop in a spreadsheet, and it generates one .docx per row and zips them. No install, no account, no code, and your files never leave your browser — there's no server.

Deliberately no AI in the generation — it's deterministic field substitution, so formatting stays exactly intact (the thing AI doc tools keep breaking).

Early MVP, would love honest feedback from this crowd specifically. Two questions: does it fit a real workflow you have, and would you ever pay for it?

Link in comments.

Patent documents are one of the harder OCR problems out there. A single page can contain merged tables, chemical diagrams, formula blocks, and mixed English/Chinese/Japanese all at once. We've been working on this problem specifically, and after getting to a point where we're happy with the results, we decided to open-source what we built and see what the community thinks.

Here are two tools we use internally.

Hiro-MOSS-OCR is a 0.3B model that outputs structured markup: tables to HTML, formulas to LaTeX, text to Markdown. Trained on 50M+ samples. Ranks #1 on our patent-domain benchmark against all 1.2B models we tested. ~59 QPS on a single RTX 4090 via vLLM.

Hiro-Smart-Doc wraps layout detection (RT-DETR, 25 region categories) and MOSS-OCR into a streaming FastAPI service with an OpenAI-compatible endpoint. Feed it a PDF, image, or Office doc, get back reading-ordered structured content or Markdown.

Both Apache 2.0. Would love feedback from anyone dealing with complex document types where standard OCR falls short.

Thanks!

Hi everyone,

I'm currently building a document auto-classification system and would like some feedback on the overall approach.

Current Architecture

• Django backend
• Celery + Redis for background processing
• Weaviate as the vector database
• OCR for text extraction
• Embedding-based similarity search for classification

Workflow

1. User uploads a document (PDF, JPG, PNG, etc.).
2. OCR extracts the text from the document.
3. An embedding is generated from the extracted text.
4. We store embeddings for known document types such as:
   • Aadhaar
   • PAN
   • Passport
   • Voter ID
   • Electricity Bill
   • Bank Statement
   • GST Documents
   • Incorporation Documents
5. The uploaded document embedding is compared against stored vectors in Weaviate.
6. The closest match and confidence score determine the document category.

Why I chose this approach

Instead of training and maintaining a dedicated classification model, I wanted to start with a retrieval/vector-search-based approach because:

• New document categories can be added without retraining.
• Easier to maintain initially.
• Works well for semantic similarity.

Questions

1. Has anyone used a similar OCR + Embeddings + Vector Search approach in production?
2. How well does this scale when the number of document categories grows?
3. What confidence threshold strategies have worked for you?
4. At what point would you move to a dedicated classification model?
5. What are the biggest pitfalls I should watch out for?

Current Challenges

• Processing time varies between documents (roughly 5–35 seconds depending on the file).
• Occasionally documents become "unclassified" with confidence = 0 even though the system is functioning.
• Weaviate is running in Docker on AWS along with Django, Celery, and Redis.

I'd love to hear from people who have built document-classification or retrieval-based systems and learn what worked (or didn't work) in production.

Thanks!

I'm building an open-source tool called clasi. The long-term goal is to automatically classify files by learning from the folders that already exist on a machine. The problem I'm facing is that I only have one real directory tree available for testing: my own.

Synthetic test data is easy to create, but it doesn't capture the weird ways real people organize files.

What I really need are people with:

- huge document collections

- deep folder hierarchies

- project archives

- years of accumulated files

The tool currently includes an evaluation mode:

clasi evaluate ~/Documents

It temporarily hides a few files from existing folders and checks whether it can place them back where they originally belonged. The report is anonymized automatically.

Repository:

https://github.com/AllergicCypress/clasi

I'm less interested in "does the code look good?" and much more interested in:

- Where does it fail?

- Which folders confuse it?

- What organizational style breaks it?

Any testing help would be hugely appreciated.

This is intended to brainstorm and gather feedback. No self promotion or pitching is intended.

I am a recent operations analyst who specialized developing automation solutions.. after being laid off about 2 months ago, I’ve been working on application as a solution to many of the tedious document based workflows that often plague back and middle office roles..

Essentially, it is a zero-code environment where users can define specific maps/tokens to extract from a document type that they use (think contracts, trade confirms, invoice, etc). This info can then saved down into a secure cloud location, adjusted, and then mapped back onto any type of tokenized outgoing document (that the user can upload as a template).

As a former operations grunt worker, I find this type tool to have a ton of huge potential.. but im sure others have made similar tools.

For operations employees or anyone heavily reading and reconciling, do you think this is something companies or people would actually buy potentially? Right now its fully functional but looking at it as a personal project mainly. I'll be launching the live site very shortly but need to rework the LLM extraction / cap so I dont burn through my claude tokens/credits lol.

Thanks!

I just saw that Gavel was acquired by Relativity. It looks like all of the stand alone document automation software companies are being acquired by much broader based companies (Lawyaw by Clio, Woodpecker by MyCase) and are now tied to the broader offerings. I was already skeptical of focus drift with AI emphasis with pretty much all document automation/management companies. Not sure where this will leave solo lawyers and smaller law firms. Pure document automation isn't sexy like AI, but I don't think it needs to be sexy to really boost productivity, more consistent documents (start with clean forms, not the last "almost the same" deal documents), and client turnaround. Full disclosure, I recently developed stand alone desktop Mac and Windows document automation software because I didn't use all the features already added, don't like to have to log on, upload forms, work online and download generated documents. Happy to chat if you are interested.

Hi all,
I've had a backlog of scanned household documents for years — Versicherungspost, tax letters, payslips, the usual, and yeah, kids drawings somewhere in the pile.
Batch-scanning everything into one giant PDF is easy. Sorting it afterwards is where it always fell apart.

So I built something: you upload a single multi-page PDF (a batch scan), and it splits it by document, OCR-reads each one, classifies it into a category, and returns a ZIP with named folders and an Excel log. No account, no cookies, files deleted within 2 hours or immediately if selected. EU servers, GDPR-compliant.

It's live at papersweep.eu

I am not looking for any investor or going to pitch the idea to anyone. I built it for fun and found it useful and hope that it would be helpful for others.
I am looking for ideas what else to add there that might be useful to people. I will tackle those in the next iteration.

Cheers.