Best OCR for Bank Statements: Open Source OCR Tools Compared for Accounting Teams

Short answer: as of August 2026 none of the leading open-source OCR projects will hand you a bank statement as a spreadsheet. Docling, DeepSeek OCR, olmOCR, Tesseract and Unstructured all read the page well, and all of them stop at text, Markdown or JSON. Tesseract can write a TSV of word positions and Unstructured can write a CSV of document elements, but neither is a transaction register. The gap between a parsed page and a workbook an accountant will sign off on is code you write and keep writing.

This comparison is aimed at people evaluating whether to build statement extraction in house on free tools. It is not a general OCR benchmark. The only question here is how much of the distance to a reconciled spreadsheet each project actually covers, because that is the part that decides whether the project takes an afternoon or becomes a permanent maintenance line.

Open source OCR for bank statements, compared

Every figure below was pulled from the GitHub API and the official documentation on 31 August 2026. Star counts and commit dates move, so re-check them before you commit to a stack.

Why none of them export a spreadsheet

This surprises people, so it is worth stating plainly. These projects were built to feed language models, not accountants. Their job is to turn a page into tokens a model can reason over, and Markdown or JSON is the right shape for that. A spreadsheet is the wrong shape for a model and the right shape for a person, which is why the tooling splits along that line.

The two apparent exceptions prove the rule. Tesseract writes a TSV, but each row is a word with its bounding box and confidence score, so reconstructing a transaction means grouping words back into cells yourself. Unstructured writes a CSV, but the columns are element type, text and element id, so a whole statement register arrives as a single row typed as a table rather than one row per transaction. Both are real outputs. Neither is a register.

What is the best OCR for bank statements?

If you are an engineering team and the files cannot leave your network, Docling is the strongest starting point. It is MIT licensed, it had a commit yesterday, it has real OCR through Tesseract, RapidOCR or Surya, and it does genuine table structure detection rather than a text dump. DeepSeek OCR is the better choice if raw throughput at enormous scale is the constraint, since its authors report more than 200,000 pages per day on a single A100-40G. If the deliverable is a spreadsheet and the person doing the work is a bookkeeper rather than a developer, none of these is the answer and a purpose-built converter is.

What do you still have to build on top?

The list is the same whichever project you pick, and it is where the schedule goes.

• Tables into one continuous register. A statement is one ledger printed across pages. You get a result per page, with repeated column headers that must be dropped rather than counted as transactions.
• Sign normalization. Separate debit and credit columns at one bank, a single signed column at another, a trailing CR marker at a third. The rules do not generalize between institutions.
• Dates with no year. Registers print 12/28 then 01/03. The year comes from the statement period, and a period crossing December means incrementing partway down the column.
• Wrapped descriptions. A long ACH memo spilling to a second line arrives as a row with no amount. Merge it wrongly and you have invented a transaction.
• Summary blocks that look like transactions. Fee summaries, interest tables and daily balance grids are also tables on the page.
• Footing the balance. Opening plus credits minus debits has to equal the printed closing balance. Without that check, an extraction that drops one row in four hundred looks perfect and is wrong in a way that surfaces during an audit.

Does open source OCR cost less than a converter?

Only if you already have the engineers and the hardware. The licenses are free, so the sticker price is zero, and that is where most comparisons stop. The real costs are a GPU for the model-based options, developer time to build the six items above, and an owner for the maintenance, because every new bank layout arrives as an edge case without warning. A bookkeeping practice with no engineer is looking at the entire cost, not a discount. A platform team already running GPU inference for other reasons may genuinely be looking at close to zero.

Volume is the honest tiebreaker. Below a few thousand pages a month, per-page pricing is cheaper than the first sprint of engineering. Well above that, and especially in the millions of pages, self-hosting wins on economics and the only question is whether you want to own the accuracy problem.

Is DeepSeek OCR still maintained?

The original repository has not had a push to its main branch since 27 January 2026. That looks like abandonment until you notice what shipped that day: DeepSeek announced the successor, DeepSeek-OCR2, in a separate repository. Attention moved rather than stopped. Two practical consequences follow. Most articles about DeepSeek OCR describe v1, and the license changed between versions, from MIT to Apache 2.0, so confirm which one you are evaluating.

olmOCR is in a similar position, last pushed in March 2026, which is neither fresh nor alarming for a research toolkit from the Allen Institute. Docling, Unstructured, Tesseract and Unstract all had commits within the last week.

Which license can a US business actually use?

Worth a compliance check before anyone writes code. Tesseract, olmOCR and Unstructured are Apache 2.0. Docling and DeepSeek OCR v1 are MIT. All five are permissive and unremarkable to a legal team. Unstract is AGPL 3.0, which is a copyleft license with a network clause, and plenty of companies prohibit AGPL outright for anything touching a hosted product. That is not a criticism of the project, it is a licensing decision you want to discover before the build rather than during review.

How accurate is open source OCR on statements?

Accurate enough to be dangerous, which is the point everyone misses. The DeepSeek authors report roughly 97 percent decoding precision under moderate compression, and that is a good number. On a page of prose, 97 percent is invisible. On a statement page carrying forty transactions of dates, descriptions and amounts, a few wrong characters per page means a few wrong numbers, and a wrong number does not look wrong. It looks like a transaction.

This is why the arithmetic check matters more than the benchmark score. Adding opening balance plus credits minus debits and comparing it against the printed closing balance is what separates output that looks right from output you can sign. No general OCR project does that, because none of them know they are looking at money.

How to choose

Ask who operates it and what the deliverable is. An engineer, GPUs available, documents far beyond statements, files that cannot leave the building: use Docling, or DeepSeek OCR if throughput dominates. A bookkeeper, an accountant or an analyst whose output is a workbook that has to foot: use a converter built for statements and spend the engineering budget somewhere it differentiates the business. Teams running both are common and sensible, with an open model for the general corpus and a purpose-built tool for the financial documents where the numbers have to hold.

If the wider need is enterprise document data extraction across contracts, forms and invoices rather than statements alone, that is a different shape of problem and worth scoping separately before you pick an OCR engine for it.

For a closer look at individual projects, the DeepSeek OCR alternative page covers its API situation and resolution modes in detail, the Docling alternative covers the strongest library option, and the Unstructured.io alternative explains why its CSV gives one row per element rather than one per transaction. On the commercial side, the Reducto alternative and LlamaParse alternative cover managed platforms, and OCR API pricing for bank statements puts real per-page numbers against each vendor. If the spreadsheet is the deliverable today, the bank statement converter is free to try on a real file.