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Padh Ke Batao is a local document explainer designed to answer a practical question: what does this letter mean, what should I do, and by when? Its author, Rahul Kumar, built it after his grandfather handed him an Election Commission Special Intensive Revision (SIR) voter-roll notice he had trouble reading. A user selects a document photo and receives a plain-language explanation with any action steps, deadline, and urgency the system identifies.
What Padh Ke Batao does
“Padh ke batao” (पढ़ के बताओ) means “read it and tell me.” Rather than asking someone to transcribe a paper first, the project takes a photo and aims to explain it in simple Hindi or English. Kumar describes examples such as bank notices, hospital reports, pension circulars, and government letters.
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The structured response is designed to identify the document type and sender, summarize its contents, highlight key details, and state whether action is needed. It also includes a deadline and an urgency category. The point is not merely to turn printed text into digital text; it is to help someone understand what the paper appears to ask of them.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsKumar also describes a Hindi read-aloud option, intended to make the explanation accessible to someone who would rather listen than read a screen.
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How the local setup works
In Kumar’s account, the vision model is the open-weight qwen3.5:4b, run locally through Ollama. A small Fastify server listens on 127.0.0.1, sends the selected photo and a JSON schema to Ollama, then streams the structured response to the web page. The schema includes document_type, summary, key_details, action_needed, deadline, and urgency.
Kumar reports that the model is 3.4 GB and that he ran it on an ordinary laptop without a GPU. He says, “The photo never leaves the machine.” That describes the project’s intended local architecture, not an independent audit of its code, network traffic, or security; local inference by itself should not be treated as proof that a particular installation has no external data flow.
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What the author’s tests do—and do not—show
Kumar reports final processing times of 56–91 seconds across six document runs. Those are observations from a small author-reported set, not a general speed promise or a benchmark across laptops and document types. He says the project resizes images to a budget of approximately 900,000 pixels to balance legibility and processing practicality.
One reported failure mode matters more than the timing: reducing dense Devanagari text too much led the model to confidently fabricate information, while full-resolution input read that sample correctly. This is a warning about relying on an apparently fluent explanation when the source text is small or crowded. The six examples do not establish broad accuracy, and no independent accuracy benchmark or user study is reported.
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Use it as an aid, not the final authority
A generated summary can make an unfamiliar notice easier to approach, but it should not replace checking the original document—especially when a mistake could affect a legal right, medical decision, financial obligation, pension, or election matter. Compare any stated deadline, amount, required document, or action against the printed notice. If the consequence is significant or the text is unclear, ask the issuing office or a trusted qualified person to verify it.
This caution follows from the project’s limited reported testing and its documented dense-text failure, not from a claim that the software always gets such documents wrong. Its useful role is to help a reader understand what a paper may say and identify questions to verify, rather than to make a consequential decision on the reader’s behalf.
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- AVOIDS PAPER JAMS AND DAMAGE. Features a brake roller system to feed paper smoothly, a multi-feed sensor that detects pages stuck together, and skew detection to prevent paper damage and data loss
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