Bengali OCR has moved from an academic curiosity to something shipping inside banks, NGOs, and government workflows in Bangladesh. This article is a working field view of what performs well in production right now, where accuracy drops off, and how teams are stitching OCR into real document pipelines.
What we're covering
- Model landscape — Tesseract, Google Document AI, local fine-tunes, and open-source alternatives for Bangla script.
- Where Bengali OCR still struggles — handwritten forms, mixed English-Bangla documents, low-resolution scans.
- What a realistic accuracy budget looks like for KYC, invoice, and ballot workflows.
- Integration patterns — scan station capture, review-and-correct loops, and export to downstream systems.
- Case notes from Captura and XDOCS AI DMS deployments.
Full write-up is being expanded. In the meantime, if you're scoping a Bengali OCR project, we're happy to walk through what we've learned from live deployments.
Want to go deeper?
This post is being expanded. If you're scoping a project in this area for a business in Bangladesh or the GCC, we're happy to share the current draft and what we've learned from live deployments.
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