Do coding agents recommend Tesseract OCR?
Agents raised Tesseract OCR in 386 judged document processing sessions and chose it alone in none, and only as part of a joint choice in 5.
Coding agents raised Tesseract OCR in 386 of 801 judged document processing sessions and chose it alone in none of them, and only as part of a joint choice in 5.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in document processing inside a realistic codebase. Not what a chat assistant says about Tesseract OCR. What an agent actually installed.
The numbers
| Category | Document processing |
| Sessions in the category | 801 |
| Sessions where Tesseract OCR was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 386 |
| Chosen when considered | 0% |
| Site | github.com |
Which agents raised Tesseract OCR
Every agent considered it and none chose it alone. Muse Code raised it most often, in 75% of its document processing sessions.
| Agent | Sessions | Raised Tesseract OCR | Chose it alone |
|---|---|---|---|
| Claude Code | 180 | 71 (39%) | 0 |
| Codex | 235 | 39 (17%) | 0 |
| Cursor | 180 | 130 (72%) | 0 |
| Grok Build CLI | 30 | 14 (47%) | 0 |
| Muse Code | 176 | 132 (75%) | 0 |
Which buyers it came up for
It surfaced most for requests written as vibe coder, in 59% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.
| Who is asking | Sessions | Raised it |
|---|---|---|
| Vibe coder | 68 | 40 (59%) |
| Junior developer | 327 | 177 (54%) |
| Senior engineer | 270 | 121 (45%) |
| Enterprise team | 136 | 48 (35%) |
What Tesseract OCR was up against
The full ranking in document processing, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Azure AI Document Intelligence | 193 | 24% |
| 2 | Anthropic Claude | 150 | 19% |
| 3 | Amazon Textract | 103 | 13% |
| 4 | Google Cloud Document AI | 49 | 6% |
| 5 | OpenAI models | 46 | 6% |
| 6 | Google Gemini | 45 | 6% |
| 7 | Docling | 24 | 3% |
| 8 | Apache PDFBox | 23 | 3% |
What this means
Raised 386 times and never chosen alone is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 386 times and made it into 5 joint choices, but never into a choice of its own.
In our data the causes, in the order they occur:
- The quickstart does not run when pasted, so the agent abandoned it mid-integration.
- The documentation describes an interface that changed, so the generated code failed.
- The product name and the package name differ, so the install step went wrong.
- The fit was genuinely wrong for the repository, which is fine and worth knowing.
All but the last are fixable in days, and the reason is written down in the session transcript.
Where these numbers come from
The 801 sessions in document processing are part of a published set of 15,051, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every document processing run can be replayed on the board.
If you work on Tesseract OCR: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend Tesseract OCR?
They raise it but rarely choose it. Across 386 sessions where Tesseract OCR came up as a candidate, agents never chose it alone; it was part of a joint choice in 5.
Does Claude Code recommend Tesseract OCR?
In 0 of the 180 sessions in document processing run with Claude Code, which is 0%.
Do different coding agents treat Tesseract OCR differently?
Not much. Claude Code, Codex, Cursor, Grok Build CLI and Muse Code chose it at similar rates, between 0% and 0% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 92 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
Where this comes from
Armature ran 15,051 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 92 realistic codebases, and published every run. The numbers on this page come from that work.