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The first seven posts in this series covered open-source tools exclusively: from the three-layer staircase selection framework, through MarkItDown and anydoc at the conversion layer, PyMuPDF / pdfplumber at the extraction layer, MinerU / Marker / Docling at the parsing layer, to Agentic Parsing for agent-driven tool selection. The conclusion was always the same: pick the right layer first, then pick the tool.
But enterprise scenarios create three forces that push you from open source toward commercial APIs:
- No GPU appetite. Parsing-layer open-source tools all require GPUs — MinerU recommends 8GB+ VRAM, Docling's VlmPipeline needs more. Not every team has the ML Ops capability to maintain an inference cluster.
- SLA requirements. Open-source tools come with no uptime guarantees and no support. Wrapping them in a microservice means 99.9% availability is your problem.
- Compliance. Finance, healthcare, and government scenarios demand SOC 2, HIPAA, or FedRAMP. The Big Three clouds' document parsing services carry these certifications natively; open-source deployments need to add them yourself.
This post organizes commercial options into three routes — specialized document parsers, Big Three cloud prebuilt services, and general-purpose VLMs — and maps them back into the three-layer staircase framework, comparing pricing, capabilities, and cost models, ending with a decision tree.
Six Major APIs at a Glance
| Positioning | Price (per 1K pages) | Output | Strength | Key Limitation | |
|---|---|---|---|---|---|
| Cohere Parse v5 | Dedicated document VLM | API $1.50; Model Vault priced separately | Markdown / Blocks | 2.3B params, lowest cost tier, private deployment | No confidence scores, no JSON output, charts excluded |
| LlamaParse | Multi-tier agentic parsing | Fast $1.25 → Agentic Plus $56.25 | Markdown / JSON | Four tiers, ParseBench 84.9% highest | Agentic mode costs 45× Fast |
| Azure DI v4.0 | Full-featured cloud | Read $1.50, Prebuilt $10, Custom $30 | JSON (structured fields) | 20+ prebuilt document models, annual commitment down to $0.53 | Cost escalates fast with feature stacking, weak on charts |
| Google Document AI | GCP ecosystem | OCR $1.50, Invoice $10, Custom $30 | JSON (structured fields) | Custom Extractor, Document AI Workbench | Lowest table accuracy of the six (64.6%), hosting fees |
| AWS Textract | AWS ecosystem | Text $1.50, Tables $15, Forms $50 | JSON (structured fields) | Analyze Lending (mortgage packets, exclusive), Expense | Highest per-page cost for Forms, no custom model training |
| Reducto r-1 | Developer-first agentic | Parse $10, Extract $20, Deep Extract $40 | Markdown / JSON | Highest table accuracy at 90.2%, citation grounding | Moderate overall ParseBench score (67.8%), newer platform |
A structural difference worth noting:
Cohere and LlamaParse output Markdown, designed for RAG pipeline ingestion — converting documents into LLM-readable text. The Big Three clouds and Reducto output structured JSON, designed for field extraction — pulling "vendor name," "amount," and "date" from invoices.
This matters more than it looks. If your use case is "dump PDFs into a vector database for semantic search," Markdown output is ready to use. If it's "automate bookkeeping from a stack of invoices," structured JSON saves you from writing a parser. Choosing the wrong output format adds an order of magnitude in downstream engineering.
Other Notable Commercial Options
Beyond those six, several others are worth putting on the evaluation shortlist:
| Price (per 1K pages) | Differentiator | Best For | |
|---|---|---|---|
| Mistral OCR 4.1 | $4 (real-time) / $1 (batch) | Built-in confidence scores + bounding boxes, handwriting accuracy 88.9% (vs Azure 78.2%), ParseBench 74.5 | Batch mode cheaper than Cohere, with confidence scores |
| Upstage Document Parse | $10–30 | TEDS table structure 93.48%, 0.6 sec/page | CJK documents, very high table accuracy needed |
| ABBYY Vantage | $20–80 (annual negotiated) | 30 years of OCR experience, most mature on-prem deployment | Large-scale financial/insurance IDP projects |
| Unstructured.io | $15 (full pipeline) | Not just a parser — an ETL platform (chunking + embedding + connectors) | One-stop ingestion pipeline |
| Mathpix | $10 | Unmatched LaTeX formula recognition | Academic papers, math/science documents |
| LandingAI ADE Gen2 | Credit-based (~1 credit/page) | Agentic extraction, DocVQA 99.16% | Agentic field extraction |
| Nanonets / Docsumo | $299–500+/month | No-code UI, built-in validation workflows | Small volume, SMBs needing human review loops |
Mistral OCR 4.1 deserves special mention: batch mode at $1/K pages is cheaper than Cohere Parse ($1.50), and it provides confidence scores (Cohere doesn't). ParseBench overall 74.5 trails LlamaParse Agentic (87.0), but the cost is 1/12th. For high-volume batch ingestion where you don't need peak accuracy, Mistral OCR batch mode may be the best cost/performance option currently available.
Using General-Purpose VLMs as Parsers
Beyond specialized parsers, there's a fundamentally different approach: screenshot each PDF page and feed it to Claude, GPT, or Gemini with a prompt to convert it to Markdown or JSON.
This isn't a hack. On the latest ParseBench leaderboard (180+ pipelines), general-purpose VLMs have broken into the specialized parser rankings:
| Rank | Method | ParseBench Overall | Type |
|---|---|---|---|
| 1 | LlamaParse Agentic Plus | 90.20 | Specialized parser |
| 2 | LlamaParse Agentic | 87.01 | Specialized parser |
| 3 | Pulse Ultra 2 | 81.60 | Specialized parser |
| 4 | LlamaParse Cost Effective | 80.61 | Specialized parser |
| 5 | Anthropic Fable 5.1 | 78.92 | General VLM |
| 6 | oi-parser | 78.30 | Specialized parser |
| 10 | Extend 2.0 | 75.33 | Specialized parser |
But ParseBench covers enterprise documents (insurance, finance, government) with limited difficulty. The harder Dr.DocBench (arXiv:2606.01393, 2026-06, 4,514 expert-level pages across 52 subjects) brings frontier VLMs down to ~60:
| Model | Dr.DocBench Overall | Table TEDS |
|---|---|---|
| GPT-5.5 | 61.94 | 48.90 |
| Claude Opus 4.6 | 60.19 | 49.21 |
| Gemini 3.1 Pro | 60.13 | 51.26 |
| MinerU 2.5 (pipeline) | 54.37 | 55.85 |
Two key signals: first, frontier VLMs cluster around 60 on expert-level documents with no clear winner; second, the pipeline-based MinerU 2.5 actually beats every VLM on Table TEDS (55.85) — specialized tools remain more reliable for table structure.
Unstructured's SCORE-Bench (2026-08, 224 real documents) further breaks down the gap: specialized pipelines beat raw VLMs by 4–16 percentage points on composite metrics, with table extraction lagging by up to 23 points. Hallucination rates also vary dramatically — Claude Opus 4.6 (tested version) at just 0.044 (on par with pipelines), but GPT-5.2 at 0.167 and Gemini 2.5 Pro at 0.257. These numbers reflect the model versions tested; newer versions may have improved, but the conclusion that "hallucination tendencies vary widely across VLM families" stands.
VLM Strengths: Chart Comprehension and Flexibility
On ParseBench's chart dimension, only 4 out of 14 specialized parsers exceeded 50%; most scored below 6%. General-purpose VLMs lead by a wide margin here — they don't just "recognize" text in charts but can "describe" what the chart conveys. This is something specialized parsers simply cannot do.
The other advantage is flexibility: a prompt change can adjust the output format, no API update needed. Markdown today, JSON tomorrow, "extract only items over $100K" the day after — VLMs handle all of these, specialized parsers don't.
VLM Weaknesses: Cost, Stability, Hallucination
Per-page cost varies enormously. For a typical document page image (~1,750 input + 500 output tokens), based on September 2026 pricing:
Specialized parsers:
| Method | Cost per 1K pages |
|---|---|
| Mistral OCR batch | $1.00 |
| Cohere Parse v5 | $1.50 |
General-purpose VLMs:
| Model | Cost per 1K pages | Notes |
|---|---|---|
| Gemini 3.8 Flash | ~$3 | Promotional pricing through 2026-12-31; ~$6 after |
| Claude Haiku 4.5 | ~$4 | |
| Claude Sonnet 5 | ~$9 | |
| Gemini 3.1 Pro | ~$10 | Preview |
| Claude Opus 5 | ~$21 | |
| GPT-5.5 | ~$24 |
Using VLMs for document parsing costs 2–16× more than specialized parsers.
The cost spread across VLMs is wide. Gemini 3.8 Flash ($3/K pages, promotional pricing through end of 2026) is the best value VLM option, approaching specialized parser costs; Sonnet 5 and Gemini 3.1 Pro ($9–10/K pages) are the mainstream choices that balance accuracy and cost; Opus 5 and GPT-5.5 (~$21–24/K pages) have the highest accuracy but cost 14–16× more than specialized parsers. Batch APIs offer discounts (roughly half price for most), but high-end VLMs remain substantially more expensive.
And general-purpose VLMs have three problems specialized parsers don't:
- Output instability. The same page run twice may produce different Markdown formatting. Tables sometimes use
|delimiters, sometimes HTML<table>. Production pipelines can't tolerate this non-determinism. - Hallucination. SCORE-Bench (2026-08, model versions tested at that time) showed Gemini's hallucination rate at 0.257, GPT at 0.167 — every four to six pages, one will contain plausibly fabricated content. The Claude family had the lowest hallucination rate (0.044), but also the highest cost. Newer model versions may have improved, but hallucination risk is a structural weakness of general-purpose VLMs.
- No confidence scores. VLMs don't tell you which fields they're uncertain about. You have to design your own quality-checking process.
When to Use VLMs vs Specialized Parsers
| Scenario | General VLM | Specialized Parser |
|---|---|---|
| Chart-heavy (tech manuals, research reports) | ✅ Only route that understands charts | ❌ Chart dimension nearly zeroed out |
| Low volume + high heterogeneity (<10K pages/month) | ✅ Not worth building a pipeline | ❌ Over-engineering |
| Need "comprehension" not just "transcription" | ✅ Can ask "what are the risk clauses in this contract?" | ❌ Only converts to text |
| High-volume homogeneous docs (100K invoices/month) | ❌ $900+/month (Claude Sonnet 5) | ✅ $150–1,500/month |
| Need structured JSON | ❌ Unstable formatting | ✅ Fixed schema |
| Zero hallucination tolerance | ❌ Hallucination rate 0.04–0.26 (varies by model) | ✅ Leaves blanks or flags low confidence |
| Table structure accuracy | ❌ Dr.DocBench TEDS ~49–51 | ✅ MinerU 55.85, Reducto 90.2% |
The Practical Approach: Multi-Model Routing
The smartest approach isn't either/or — it's routing: standard pages go through specialized parsers (Cohere / Mistral OCR), complex pages (charts, handwriting, unusual layouts) upgrade to Gemini 3.8 Flash or Sonnet 5, and only the truly difficult cases justify Opus 5 / GPT-5.5. This aligns perfectly with Agentic Parsing — let an agent choose the route based on page characteristics rather than one-size-fits-all.
Per Unstructured's analysis, specialized pipelines' advantage isn't a better model but three layers of wrapping: optimized prompting + post-processing + output enforcement. In other words, if you're willing to build those three layers yourself, using a general VLM as the underlying engine is viable — but at that point you're essentially building your own specialized parser.
Capability Comparison by Dimension
Tables
Tables are the litmus test for document parsing. Per Reducto's RD-TableBench (1,000 complex tables), accuracy rankings:
| Method | Table Accuracy |
|---|---|
| Reducto r-1 | 90.2% |
| Azure DI | 82.7% |
| AWS Textract | 80.9% |
| Cohere Parse 5 (ParseBench Tables) | 87.0% |
| Google Document AI | 64.6% |
The two benchmarks use different datasets and metrics, so Cohere's 87.0 (ParseBench) and Reducto's 90.2 (RD-TableBench) aren't directly comparable. But Google placing last in both tests is a reliable conclusion.
Charts
On ParseBench's chart dimension, only 4 of 14 methods exceeded 50%, with most specialized parsers below 6%. Cohere Parse v5 explicitly excludes chart extraction. If your documents contain many charts (tech manuals, research reports), the most reliable approach is to use general-purpose VLMs (Claude, GPT-5, Gemini) to describe chart contents — see the "Using General-Purpose VLMs as Parsers" section above for the detailed comparison.
Handwriting Recognition
All three clouds support handwriting recognition (Azure DI and Textract list it explicitly), and Mistral OCR 4.1 achieves 88.9% handwriting accuracy (vs Azure's 78.2%). Cohere, LlamaParse, and Reducto don't specifically mention handwriting support. For scenarios involving handwritten forms (healthcare, insurance claims), Mistral OCR and the Big Three clouds are all viable options.
Multilingual Support
| Method | Languages |
|---|---|
| LlamaParse | 100+ |
| Cohere Parse | 9 commercial languages |
| Azure DI | 300+ (Read OCR) |
| Google Document AI | 200+ (OCR) |
| AWS Textract | EN/ES/DE/FR/IT/PT |
Azure and Google have the broadest OCR language coverage, but this is character-level recognition — it doesn't mean structured extraction supports that many languages. Cohere trained on only 9 languages; whether Traditional Chinese is included needs verification. AWS Textract has the narrowest language support — watch out for non-Latin scripts.
Form Key-Value Extraction
This is where the Big Three clouds shine. Azure DI has 20+ prebuilt models (invoices, receipts, IDs, contracts, bank statements, checks, tax forms), Google Document AI has similar specialized processors, and AWS Textract has the exclusive Analyze Lending (mortgage packet automation) and Analyze Expense.
Cohere and LlamaParse don't do KV extraction — they convert documents to Markdown, and you use an LLM to extract fields. Reducto's Extract API does structured extraction, but without prebuilt document type models — you define the schema yourself.
Confidence Scores
| Method | Has Confidence Scores |
|---|---|
| Azure DI | ✅ (per field) |
| Google Document AI | ✅ (per field) |
| AWS Textract | ✅ (per field) |
| Mistral OCR | ✅ (per field + bounding box) |
| Reducto | ✅ (citation grounding) |
| LlamaParse | ❌ |
| Cohere Parse | ❌ |
| General VLMs | ❌ |
Confidence scores are a hard requirement for enterprise scenarios. Without them, you don't know which pages need human review — either review everything (cost explosion) or trust the machine blindly (quality disaster). Cohere's documentation explicitly lacks confidence scores, a significant limitation for high-value documents (contracts, legal documents).
Cost Model: 100K Pages/Month
List prices are just the starting point. Actual costs can differ by 10× depending on the scenario.
Scenario A: Plain Text OCR (single-column documents, no table structure needed)
| Method | Monthly Cost |
|---|---|
| LlamaParse Fast | $125 |
| Cohere Parse 5 | $150 |
| Azure DI Read | $150 |
| Google OCR | $150 |
| AWS Textract DetectText | $150 |
All five are nearly identical. This scenario shouldn't use commercial APIs at all — extraction-layer pymupdf4llm handles it at zero cost. If you insist on using VLMs: Gemini 3.8 Flash costs $300/month, Claude Sonnet 5 costs $900/month — 2–6× the price for the same job, plus hallucination risk.
Scenario B: Mixed Documents with Tables
| Method | Monthly Cost | Type |
|---|---|---|
| Cohere Parse 5 | $150 (tables included in base price) | Specialized parser |
| Mistral OCR batch | $100 | Specialized parser |
| LlamaParse Agentic | $1,250 | Specialized parser |
| Azure DI Prebuilt | $1,000 | Big Three cloud |
| AWS Textract Tables | $1,500 | Big Three cloud |
| Reducto r-1 Parse | $1,000 | Specialized parser |
| Gemini 3.8 Flash (VLM) | $300 | General VLM |
| Gemini 3.1 Pro (VLM) | $1,000 | General VLM |
| Claude Sonnet 5 (VLM) | $900 | General VLM |
| Claude Opus 5 (VLM) | $2,100 | General VLM |
The gaps widen. Cohere and Mistral OCR batch are cheapest because their base price includes tables — but the trade-off is no structured JSON (Cohere also lacks confidence scores). Lightweight VLMs (Gemini 3.8 Flash at $300) are close in cost but lower in accuracy and stability; mid-range VLMs (Sonnet 5 at $900, Gemini 3.1 Pro at $1,000) deliver practical accuracy but cost 6–7× more than specialized parsers. For "convert tables into usable Markdown," Cohere / Mistral batch offers the best value; for "extract specific fields from tables," the Big Three cloud prebuilts are the right choice.
Scenario C: Invoice/Receipt Automation (KV extraction needed)
| Method | Monthly Cost |
|---|---|
| Azure DI Invoice Prebuilt | $1,000 |
| Google Invoice Parser | $1,000 |
| AWS Textract Expense | $1,000 (first 1M pages) |
| Reducto Extract | $2,000 |
The Big Three are nearly identical for prebuilt scenarios. But Azure's annual commitment can push 8M pages/month down to $0.53/K pages — significant discounts at high volume.
Hidden Costs
Beyond list prices, at least three other factors:
- Retry costs. Pages that return 500s or timeouts need re-processing. Community reports suggest 0.5–2% failure rates across providers, meaning 500–2,000 pages of retry costs per 100K.
- Human review. Methods without confidence scores (Cohere, LlamaParse) require you to design a quality sampling process. Methods with confidence scores still need human review for below-threshold pages — as yololab's analysis of Cohere Parse v5 put it, "cheap per thousand pages ≠ cheap per approved case," because back-end human review costs can exceed API fees.
- Rate limiting. Google Document AI's processors have hosting fees ($0.05/hour) that run even with no requests. Azure's commitment tier charges overages at pay-as-you-go rates. These are invisible during POC but erode your cost model in production.
How to Read Benchmarks (Again)
This series keeps repeating the same point: who made the benchmark matters as much as the scores.
- ParseBench is published by LlamaIndex; the top-ranked LlamaParse Agentic (84.9%) is their own product. Methodology is public and the dataset is on HuggingFace, but structural bias remains.
- RD-TableBench is published by Reducto; the top-ranked Reducto r-1 (90.2%) is their own product. Same structure.
- olmOCR-bench primarily evaluates open-source tools, with limited data points for commercial APIs.
No fully neutral third-party benchmark currently spans all commercial APIs. The closest is the academic survey Document Parsing Unveiled (arXiv:2410.21169, 2024), but Cohere Parse v5 and Reducto r-1 weren't released when it was published.
The pragmatic approach is to test with your own documents. Sample 50–100 pages from your actual files, covering the hardest cases, run them through each API, and manually compare outputs. 50 pages of your own evaluation tells you more than 2,000 pages of someone else's benchmark.
Open Source vs Commercial: Where's the Crossover?
It's not as simple as "self-host at high volume, pay per page at low volume." The crossover depends on four dimensions:
1. GPU Operations Capability
Have an ML Ops team → open source (MinerU / Docling) as a self-hosted microservice, marginal cost approaching zero. Don't have one → commercial API, outsource the reliability burden.
2. Document Diversity
Uniform format (all the same type of invoice) → Big Three cloud prebuilt models, one API call yields structured fields. Highly heterogeneous (contracts, financial reports, tech manuals mixed together) → LlamaParse Agentic or open-source with Agentic Parsing for custom routing.
3. Output Requirements
Need Markdown (for RAG) → Cohere Parse (cheapest) or LlamaParse (most accurate). Need structured JSON (field extraction, bookkeeping) → Big Three cloud prebuilts or Reducto Extract. Need both → chain two tools, or use Reducto (outputs both Markdown and JSON).
4. Compliance and Deployment Location
Data can't leave the country → Azure / AWS / Google regional deployments, or Cohere's Model Vault private deployment. Need SOC 2 / HIPAA → Big Three clouds have them natively; Cohere and LlamaParse depend on enterprise plans.
Decision Tree (extending Part 1)
Does your document need the parsing layer?
│
├─ No (has text, simple layout)
│ └─ [Extraction layer] pymupdf4llm, zero cost
│
└─ Yes (scanned / complex layout / table-heavy)
│
├─ Team has GPU + ML Ops?
│ ├─ Yes → [Open-source parsing layer] MinerU / Docling
│ │ Marginal cost approaches zero at scale
│ └─ No → Go to commercial options below
│
└─ Which commercial route?
│
├─ Documents contain many charts, need "comprehension"?
│ └─ Yes → [General VLM] Claude Opus 5 / GPT-5.5
│ (expensive but dominates chart dimension)
│
├─ Need structured field extraction? (invoices/receipts/IDs)
│ └─ Yes → Which cloud are you on?
│ ├─ Azure → Azure DI Prebuilt
│ ├─ GCP → Google Document AI
│ └─ AWS → Textract (mortgages → Lending)
│
├─ Need Markdown for RAG?
│ ├─ Lowest cost → Mistral OCR batch / Cohere Parse
│ ├─ Highest accuracy → LlamaParse Agentic ($12.50/K pages)
│ └─ Table-heavy → Reducto r-1 (table accuracy 90.2%)
│
├─ Mixed scenario (95% standard + 5% complex)
│ └─ Multi-model routing: standard pages via
│ specialized parser, complex pages via VLM
│
└─ Not sure → Test with 50 pages of your own data firstPre-Production Validation Checklist
Regardless of which provider you choose, run through at least these six items before going live:
1. Corpus coverage test. Sample 50–100 pages from production documents, covering both best-case and worst-case scenarios. Don't just test clean PDFs — include skewed scans, low-resolution images, and documents with stamps.
2. Table integrity. Pick the most complex tables (merged cells, cross-page, nested) and verify the output's row/column counts are correct. One missing column means the output is wrong.
3. Confidence score calibration. If the API provides confidence scores, verify they actually correlate with error rates — are fields at confidence 0.95 really 95% accurate? Or is it just model overconfidence?
4. Latency and throughput. Average latency isn't enough — test P99. API latency during peak hours can be 3–5× idle times. 100K pages/month averages under 1 page/second, but if your ingestion runs as a daily batch job, burst throughput requirements will be much higher.
5. Failure modes. Deliberately submit bad files (0 bytes, encrypted PDFs, documents exceeding page limits) and see how the API responds. A 400 error that tells you something is wrong is fine; silently returning empty results is dangerous because your pipeline won't know something failed.
6. Cost estimate multiplier. Multiply the list price by 1.3–1.5× to cover retries, rate-limit waits, and human review. If the multiplied number still fits your budget, you're safe.
Relationship to Visual RAG
ColPali takes a completely different path: skip text parsing entirely and do patch-level embedding directly on PDF page images. Table structure is 100% preserved, but BM25 is unusable and storage is ~100× larger.
Commercial APIs and ColPali aren't competitors — they serve different pipeline stages:
- Need text semantic search → use commercial API or open-source parser to convert to text → embedding → vector search
- Table-heavy, complex layouts, need visual grounding → ColPali does image-based retrieval directly
- Both → use Agentic Parsing to have the agent dynamically select routes by page type
The Bottom Line
Commercial document parsing offers more options than most people realize — not just six APIs, but combinatorial choices across three routes: specialized parsers pursuing cost efficiency, Big Three cloud prebuilts pursuing out-of-the-box field extraction, and general VLMs pursuing comprehension and flexibility.
The most counterintuitive finding: general VLMs can already crack the top five overall (Fable 5.1 scores 78.92 on ParseBench), yet they still lose to pipeline-based specialized tools on table structure accuracy (on Dr.DocBench, MinerU 2.5's Table TEDS of 55.85 beats every VLM's 48–51). More accurate overall doesn't mean more suitable — "transcription" and "comprehension" are two different needs, and choosing the wrong route costs an order of magnitude more than choosing the wrong tool.
The core selection question isn't "which is best?" but "do you need transcription or comprehension?" Transcription → specialized parser. Comprehension → VLM. Both → multi-model routing. Only after that decision do volume, team capability, and compliance requirements come into play.
There's still no silver bullet, just as every post in this series has concluded. But now you have a more complete decision tree — starting from three routes and arriving at a concrete choice.
References
- ParseBench: A Document Parsing Benchmark for AI Agents (arXiv:2604.08538)
- ParseBench Leaderboard — GitHub
- LlamaIndex — ParseBench Blog Post
- Dr.DocBench: Evaluating LLM/VLM Document Parsing (arXiv:2606.01393)
- Unstructured — Frontier Models Are Strong But Document Parsing Is Harder (SCORE-Bench)
- OCR Arena — Community Blind-Test ELO Rankings
- Cohere Parse Official Page
- Cohere Pricing
- Reducto r-1 Model Launch
- Reducto Pricing
- Mistral OCR — Official Documentation
- AWS Textract Pricing
- Azure AI Document Intelligence Pricing
- Google Cloud Document AI Pricing
- Document Parsing Unveiled: Techniques, Challenges, and Prospects (arXiv:2410.21169)
- The Three-Layer Staircase of Document Parsing (Series Part 1)
- The Parsing Layer: When Structure Must Be Inferred (Series Part 5)
- Scanned PDF Benchmark: 10 Parsers Tested (Series Part 6)
- Agentic Parsing: Let Agents Decide How to Parse (Series Part 7)
- ColPali: Skip OCR, Retrieve Documents as Images
- Table Serialization: How to Convert Tables to Text for RAG
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