developer-tools

PDF Vector

API PDF parser that accepts hybrid and scanned documents, though the output still needs cleanup before it reads like clean markdown.

Hybrid PDFScanned OCRJSON outputMarkdown extraction
TL;DR — our verdictUpdated September 2026 · 2 test artifacts

Can ingest both hybrid and scanned PDFs, but the output is not yet clean markdown

Where it wins
  • You need an API that can ingest a mixed digital/scanned PDF without failing on upload.
  • You can post-process flattened text if the parser returns markdown inside JSON.
  • You want a tool that at least completes on scanned PDFs and returns parsed text.
Main limitation
  • You need clean, directly usable markdown with preserved section and table structure out of the box.

Our take

PDF Vector accepted both the 84-page hybrid earnings report and the scanned research PDF, so the ingestion path is working. The limitation is output quality: the hybrid report came back as markdown content inside JSON with a mostly flattened structure, and the scan test only proves a successful parsed preview rather than a clearly faithful markdown export. It looks useful as a raw document parser, but this research does not show a structure-preserving converter yet.

Download video: Demo video
Walkthrough showing a search for PDF Vector, then a playground upload of Target-2015-Annual-Report.pdf and a successful parse of the document.

In-Depth Review

Our detailed analysis of PDF Vector — features, performance, and real-world testing.

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AI Demos Team
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Verified Review

Feature-by-Feature Breakdown

PDF Parsing
Accepted the file, but the output was flattened and JSON-wrapped rather than clean markdown.
▾
Test Summary
Feature tested: PDF Parsing
Result: Partial — Accepted the file, but the output was flattened and JSON-wrapped rather than clean markdown.

Feature tested: PDF Parsing

Result: Partial

Verdict: Accepted the file, but the output was flattened and JSON-wrapped rather than clean markdown.

Expected behavior: PDF Vector can ingest and extract content from PDF documents, including a complex 84-page hybrid earnings report and a scanned research PDF. The evidence shows it accepts both file types and returns parsed content, though the output quality and structure vary.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Target-2015-Annual-Report.pdf

Observed output: Output artifact (Image): The response screenshot shows the Target 2015 annual report title, financial highlights, diluted EPS figures, total segment sales, and category percentages embedded in escaped JSON-like markdown, which confirms the flattened output. — raw_json_screenshot.png

Input artifact: Input artifact (PDF document): INPUT — Target-2015-Annual-Report.pdf

Output artifact: Output artifact (Image): The response screenshot shows the Target 2015 annual report title, financial highlights, diluted EPS figures, total segment sales, and category percentages embedded in escaped JSON-like markdown, which confirms the flattened output. — raw_json_screenshot.png

What changed: PDF document transformed into Image

Test case: PDF document → Text/code file

Input type: PDF document

Input used: Input artifact (PDF document): INPUT — Scanned Research PDF.pdf

Observed output: Output artifact (Text/code file): The screen recording shows the upload completing successfully, with the run finishing in 20.6 seconds for a 12-page document and the parsed preview appearing in the results pane. — md_extracted_from_json.md

Input artifact: Input artifact (PDF document): INPUT — Scanned Research PDF.pdf

Output artifact: Output artifact (Text/code file): The screen recording shows the upload completing successfully, with the run finishing in 20.6 seconds for a 12-page document and the parsed preview appearing in the results pane. — md_extracted_from_json.md

What changed: PDF document transformed into Text/code file

Why it matters / Conclusion: Good enough to ingest a complex PDF, but not yet good enough to trust as clean markdown without extra processing.

PDF Vector can ingest and extract content from PDF documents, including a complex 84-page hybrid earnings report and a scanned research PDF. The evidence shows it accepts both file types and returns parsed content, though the output quality and structure vary.

file
Target-2015-Annual-Report.pdf
↓
image/png
Output artifact for "PDF Parsing" test: The response screenshot shows the Target 2015 annual report title, financial highlights, diluted EPS figures, total segment sales, and category percentages embedded in escaped JSON-like markdown, which confirms the flattened output., raw_json_screenshot.png
The response screenshot shows the Target 2015 annual report title, financial highlights, diluted EPS figures, total segment sales, and category percentages embedded in escaped JSON-like markdown, which confirms the flattened output.
application/pdf
Scanned Research PDF.pdf
↓
text/markdown
md_extracted_from_json.md
Loading file...
The screen recording shows the upload completing successfully, with the run finishing in 20.6 seconds for a 12-page document and the parsed preview appearing in the results pane.
Bottom Line
Good enough to ingest a complex PDF, but not yet good enough to trust as clean markdown without extra processing.
✓ Use This If
●You need an API that can ingest a mixed digital/scanned PDF without failing on upload.
●You can post-process flattened text if the parser returns markdown inside JSON.
●You want a tool that at least completes on scanned PDFs and returns parsed text.
✕ Skip This If
●You need clean, directly usable markdown with preserved section and table structure out of the box.
●You need a standalone markdown export rather than text embedded in JSON.
●You need proof of high-fidelity OCR quality from the scan test alone.
developer-toolsapistextOther
Yes. The 84-page hybrid earnings report uploaded successfully and was parsed, but the returned content was mostly flattened text embedded in JSON rather than clean markdown.
Yes. The scanned research PDF completed successfully, and the results pane showed a parsed document preview after the run.
Not in the hybrid-report test. The report says the markdown content was embedded in a JSON payload, and no standalone markdown export was provided there.
Both files were parsed successfully, but the hybrid report exposed the bigger limitation: its output was flattened and JSON-wrapped. The scan test proved ingestion and OCR-backed parsing, but the report does not show that its markdown structure was clean.

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