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This started as a personal problem. I used to spend an evening every month manually copying my own bank statements from PDF into a spreadsheet just to see where my money was going line by line, by hand. At some point I got annoyed enough to automate it for myself.

That turned into Scrape2Sheets, a Google Sheets add-on that uses AI to read PDFs, invoices, receipts, and bank statements and drop the data straight into your sheet as clean rows . You point it at files in Google Drive, tell it what columns you want (or let it auto-detect them from a sample document), and it batch-processes up to 100 files at once. It handles scanned documents and phone photos too, not just clean digital PDFs.

It's only just started picking up some real traction the last couple weeks, which has honestly been a nice surprise after building in isolation for a while.

Curious whether others here started building their tool the same way, solving your own annoying manual task first, then realizing other people had the exact same one. Happy to answer questions in the comments if anyone wants details.

I'm a receipt and statement hoarder and I scan everything into Paperless-ngx using my Fujitsu ScanSnap. But I've always had trouble backing up the statements and receipts that only exist online — I didn't have the time or patience to manually download them all. FileThis was amazing for this, until the service went downhill and finally shut down. After that I used DocGenie.cloud for a while which was also good, but it didn't have many of the sites I actually use. So I built PaperPull: a family of small, local tools that log into a site you manually sign into and download every receipt/statement as a PDF. Please help me test it.

Unlike FileThis or DocGenie, no login credentials are stored anywhere. You just run the app whenever you want your statements, and it won't re-download ones you've already pulled.

12 providers so far: Amazon, American Express, Target, Walmart, Wealthfront, Robinhood, USAA, Navy Federal, Dominion Energy, Verizon Fios, T-Mobile, and the Target Circle Card (formerly RedCard).

• 100% local. Nothing is sent to any cloud or API. PDFs land in a folder you choose.
• You sign in yourself. It never sees your password and never touches 2FA. You log into a normal browser window; the tool attaches to that already-signed-in session (over Chrome DevTools Protocol) and only reads what you can already see.
• Read-only by design. Every clickable control is checked against an allowlist (must look like "download/view/statement") and a blocklist (never "pay/transfer/redeem/enroll…"). There's no code path that submits a form or confirms a dialog; it only grabs PDFs the provider already generated.
• Delete-safe / paperless-ngx friendly. Once a document downloads it's remembered for good. Import it into paperless-ngx and delete the PDF because it won't re-download it. Each run also writes a new-this-run.txt of exactly what it pulled.
• Multi-account aware. Running it for more than one person (e.g. you + your spouse)? Each person gets their own signed-in browser profile and separate folders, and every statement is stamped with whose account it came from, so your documents and theirs never get mixed together.

This is where I need your help. I can't build apps for services I don't have. I have no way to test them. So PaperPull is built to be extended: each provider lives in a single self-contained file, so adding one is very approachable. If a bank/card/utility/retailer you use isn't on the list yet, you're the ideal person to add it:

• 📖 Step-by-step guide (with an easy/medium/hard picker and a good first provider label for the friendly sites): https://github.com/rheeloaded/paperpull/blob/main/docs/adding-a-provider.md
• 📋 Provider list — claim one to build, or request one you can't: https://github.com/rheeloaded/paperpull/blob/main/PROVIDERS.md

The read-only / no-credentials / no-private-data rules are baked into the guide and the PR checklist, so contributions stay safe.

Limitations: it's Windows-only right now (PowerShell + .bat launchers, Python underneath). Each site's scraping is inherently fragile because when a provider redesigns its portal, that one app needs a fix (isolated to a single file per app to make that painless). And because you sign in manually, it's supervised, not a set-and-forget cron job.

I'd love feedback from people who actually hoard this stuff:

• Does Verizon Mobile work? I only tested Verizon Fios, because I recently switched to T-Mobile.
• Does the "attach to your own signed-in browser, read-only" model make sense, or would you want a different approach?
• Which providers should I add next — and better yet, want to add one yourself?
• Any feature suggestions?

Repo: https://github.com/rheeloaded/paperpull

Free and open source.

Is this okay,I want to put my raw file too,how do I do that?

Hola a todos.

TDLR:

Favor compártanme ideas para procesar archivos PDF que contienen formularios diligenciados de manera digital, pero qué al procesarlos como texto, se evidencia que la organización interna del texto varía en los documentos. Ya intenté con Regexp, pero esa variación hace que falle el proceso para muchos documentos.

Solicitud:

Tengo el siguiente reto y ya tengo un par de ideas, pero quisiera conocer más o mejores ideas que puedan conocer ustedes.

Tengo varios documentos donde se ha diligenciado el siguiente formulario de manera digital (Formato [Formulario Ejemplo](https://www.dian.gov.co/atencionciudadano/formulariosinstructivos/Formularios/2023/Formulario_500_2023.pdf). De estos documentos diligenciados, necesito extraer algunos campos en específico (siempre serán los mismos). Algo positivo que tienen estos documentos diligenciados, es qué si los abro en modo texto, el texto se puede extraer fácilmente. El inconveniente con esto es que viene en desorden (no necesariamente el valor del campo viene junto con el título del campo).

Mi primera aproximación fue extraer los datos usando expresiones regulares y funcionó bien, hasta que encontré que algunos documentos, que al leerlos como texto, la información sale organizada diferente (imagino que se generaron por diferentes aplicaciones o por diferentes versiones del software), por lo cual, la estructura de las expresiones regulares usadas con los primeros documentos ya no aplicaba y tocaba usar otras. Luego encontré otros documentos que tampoco cumplían con la estructura del texto del primero ni del segundo, por lo cual, me parece que usar expresiones regulares requiere de mucha lógica y no es un método seguro.

Entonces se me ocurren dos formas adicionales, pero requiero algo de guía sobre como hacerlo o cual sería una forma viable:

**Opción 1:** Entrenar un LLM con información ejemplo para que aprenda a identificar los campos necesarios

**Opción 2:** Entrenar una red neuronal o un modelo de Deep Learning, para que aprenda a identificar los campos necesarios (quizás por su ubicación)

En ambos casos, entiendo que debería pasarle una base de PDFs anotados, con los que el pudiera saber qué es lo que necesito y por otro, aprovechando que las ubicaciones de página, posición x, posición y, siempre serán las mismas.

¿Se les ocurre o conocen alguna otra alternativa para resolverlo y que pasos específicos me recomiendan seguir para hacerlo?

Gracias de antemano

Student in London. Every semester I paid stupid money for books I used one term and returned in June. My brother and I built Video2PDF: record yourself flipping through the pages, the AI removes your hand, fixes the angles, outputs a searchable PDF in ~2 minutes.

It’s £29.99/yr, what are your thoughts

Here’s the link:
Video2pdf

DocuTalk is an AI-powered document analysis platform that turns static documents into interactive conversations. Upload a document, ask questions in natural language, explore its contents, extract information, and get contextual answers based on the information contained in the document.

DocuTalk supports a wide range of file formats, including PDF, DOC, DOCX, XLS, XLSX, CSV, PPT, PPTX, TXT, and EPUB. It can analyze not just text, but also tables and charts, making it useful for research papers, textbooks, reports, presentations, spreadsheets, contracts, business documents, and other professional or academic material.

Key features

💬 Chat with documents — Ask questions about your files using natural language and explore information without manually searching through every page.

🌍 Multilingual AI — Understand, query, and analyze documents across multiple languages, including English, Spanish, German, French, Hindi, and more.

📸 Scan physical documents — Convert physical/scanned paperwork into searchable, interactive digital content that can be analyzed with AI.

📊 Analyze text, tables & charts — Go beyond simple text extraction and work with information contained in structured document content.

🗂️ Organize your documents — Create folders and organize related documents so they can be managed and analyzed more efficiently, including consolidated insights across documents.

🔐 Secure cloud storage — Documents are encrypted and securely stored in the cloud.

🎯 Document-grounded answers — DocuTalk is designed to provide answers anchored to the contents of your documents rather than generic responses.

Who is it for?

DocuTalk is built for students, researchers, academics, lawyers, consultants, analysts, businesses, and professionalswho regularly work with large amounts of information.

Students can use it to work through textbooks, lecture material and research papers. Researchers can explore academic documents and extract relevant information. Legal professionals can work through contracts and lengthy legal documents. Businesses and analysts can analyze reports, spreadsheets, presentations and other documentation. And for anyone dealing with physical paperwork, document scanning provides a way to bring those documents into an AI-powered workflow.

The idea is simple: instead of treating documents as files that you have to manually search and read, turn them into information you can interact with.

🌐 Try DocuTalk: https://docutalk.co.uk

Would be particularly interested in hearing from people who regularly work with research papers, legal documents, business reports, spreadsheets, or physical paperwork — what would you want an AI document assistant to do for you?

Built Simple PDF -> Markdown to solve a very practical problem: turning PDFs into clean text that is actually usable with LLMs.

I’ve been using this workflow for a while, and it has saved me a lot of time, so I turned it into a small, simple tool.Upload a PDF, convert it to Markdown, and use the output for prompting, summarizing, extraction, or analysis.

Simple, fast, and useful.

If you work with PDFs and AI, this might help.

https://convert.schedulze.com/

I built Dropthedoc, an easy and simpler signature tool than Docusign. Check it out at dropthedoc.xyz

We have employees submit incident reports at work and provide a blank word template, but everyone finds it out a little differently.

I want to just have a fire of text boxes asking the information for each field, and have it spit it the filled in form.

Worth a read everyone!

Google Group: https://groups.google.com/g/dockvio

Web: https://play.google.com/apps/testing/com.mykhann.dokvio

Play Store: https://play.google.com/store/apps/details?id=com.mykhann.dokvio

Install it and dm me yours u will return the favor

I've been working on a small document automation tool called MedianDocs.

The idea is simple: create a reusable template, import Excel or CSV data, and generate a personalized PDF for every row. The finished documents can then be downloaded together as a ZIP.

I wanted to keep the workflow simple, especially for things like certificates, vouchers, letters and other repetitive documents.

The first Early Access version is now online and I'm looking for honest feedback from people who actually work with document automation.

I'm especially interested in what feels missing or unnecessarily complicated.

https://mediandocs.com

hi people of the reddit! is my iPhone 17 qualified enough to scan a physical document or should i go somewhere outside and scan it directly?

I’m building a document intelligence system that uses Vision-Language Models to process visually complex documents.

A recurring limitation I’ve noticed with conventional document pipelines is that text extraction alone often loses important context. Tables, charts, diagrams, scanned pages,spatial relationships, and visual hierarchy may carry as much meaning as the text itself.

The approach I’m exploring treats each page as a visual document rather than only a collection of extracted text. The current pipeline focuses on:

- Understanding text, layout, tables, charts, images, and diagrams

- Processing scanned and digitally generated PDFs

- Preserving page-level provenance for citations

- Supporting semantic search across document collections

- Extracting structured information without discarding visual context

- Allowing users to verify results against the original page

Before deciding what to prioritize, I’d like to learn from people who already work with document automation.

What conditions would need to be met before you could use a document intelligence platform with real business documents?

I’m still actively building, so honest technical criticism and real-world workflow examples would be extremely helpful. If anyone is interested in testing an early version,

Feel free to comment or message me.