OCR & Document Scanner App

Documents Indian businesses actually scan

These are the document types we have built extraction for most often, with the realistic accuracy on a decent phone photo in reasonable light.

Validation is where the accuracy comes from

A model that returns a value with no way to check it forces a human to check every value, which removes most of the benefit. Validation rules let the app be confident about the ninety per cent it can prove and escalate only the rest.

• Checksums. GSTIN and Aadhaar both carry check digits. A misread that breaks the checksum is caught instantly and can be re-read from a second frame.
• Format masks. PAN is five letters, four digits, one letter. IFSC is four letters, a zero, six characters. A value that does not fit the mask never reaches your database.
• Cross-field arithmetic. On an invoice, line items should sum to the subtotal and tax should be a legal rate. When they disagree, one of the two was read wrong and the app knows to ask.
• Amount in words versus figures. On cheques, the two fields must agree. This single rule catches most amount misreads.
• Master-data lookup. If the party name almost matches an existing customer, offer the match rather than creating a duplicate.

Capture quality decides everything downstream

Most of the engineering effort in a good scanner app sits in the twenty seconds before the photo is taken, not the two seconds after.

The camera screen should detect the document edge live, tell the user the frame is too dark or the phone is tilted, refuse to fire when there is motion blur, and auto-crop and de-skew the result. A three-second guided capture produces a better outcome than any amount of post-processing on a bad frame.

For staff scanning fifty documents in a row, we add batch mode: shoot continuously, review the strip afterwards, retake the two that failed. It roughly halves the time per document compared with a shoot-review-confirm loop.

Working without a signal

Godowns, basements and site offices have no usable data connection, and that is where documents get scanned.

The app queues captures locally with the extracted text and syncs when a connection returns. For simple document types we run a small on-device model so extraction happens offline too and the user gets their confirmation immediately; complex layouts wait for the server model.

The queue is visible to the user. A silent background sync that fails leaves people convinced the app ate their work, and they go back to the register.

Where the extracted data goes

An app that produces a screen full of correct fields and nothing else has not saved anyone any typing. The value appears at the point where the record lands somewhere useful.

We have pushed extraction output into Tally via XML, into Zoho and Odoo through their APIs, into Google Sheets for teams with no ERP, and into custom backends over a REST endpoint we build alongside the app. If your system has no API, the honest answer is that a scheduled CSV drop is the practical route, and we will say so rather than quoting for an integration that cannot exist.

What ships at the end

A signed Android app on your Play Store account, the extraction backend deployed on your infrastructure, the document map as living documentation, an admin view for reviewing low-confidence captures, and the full source.

We also hand over the confidence thresholds and a short note on how to tune them. Set them too high and staff review everything; too low and errors slip through. That dial belongs to you, and it usually needs one adjustment after the first fortnight of real use.