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September 22, 2026•11 min read•By Dzaki Amri Zaidaan

I Tested 7 Document Parsing APIs on Real Scanned PDFs (2026): Code and Results

A reproducible benchmark of seven document parsing APIs on 120 real scanned PDFs, measuring extraction F1, table accuracy, latency, and cost per page. Includes the evaluation harness code and the failure modes nobody publishes.

#AI & ML
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I Tested 7 Document Parsing APIs on Real Scanned PDFs (2026): Code and Results

Every "best document parsing API" post I have read this year has the same problem: nobody actually runs the APIs on the same corpus, with the same ground truth, and publishes the failure cases. Vendor benchmarks use clean, born-digital PDFs. Production data is scanned at 200 DPI, skewed 3 degrees, stamped, and full of merged table cells. The gap between the demo and the invoice that arrives from a fax machine is where your pipeline dies.

This article is the benchmark I wanted to read. I ran seven parsing APIs against 120 real scanned PDFs (invoices, bank statements, lab reports, shipping manifests) with hand-verified ground truth, and I am publishing the harness, the numbers, and the ugly parts.

The Problem & Industry Shift

For a decade, the document extraction stack was a fixed pipeline: Tesseract for OCR, then a hand-written regex or template per document type, then a human review queue. That approach has three hard limits:

  1. Layout blindness. Classic OCR returns a flat token stream. It destroys the 2D structure that makes a table a table. A line item's quantity and its price end up in the same string with no reliable boundary.
  2. Template fragility. Every new vendor layout is a new template. At 50+ suppliers, template maintenance becomes a full-time job.
  3. No semantic grounding. OCR gives you text, not fields. Mapping "Total Due" to a typed amount_due field is a separate, brittle problem.

The 2024-2026 shift is toward vision-language models (VLMs) and layout-aware transformers that consume the page image directly and emit structured output. Instead of OCR-then-parse, these models do joint layout understanding and field extraction in one forward pass. The trade-off is real: you trade deterministic, cheap, low-latency OCR for probabilistic, expensive, higher-latency inference that generalizes across layouts.

The engineering question is not "which is better." It is "for this document class, at this volume, with this accuracy SLA, what is the cheapest system that clears the bar?" That is what this benchmark answers.

Architecture & Core Mechanics

All seven APIs were wrapped behind one interface so the harness is provider-agnostic. The pipeline has four stages: ingest, normalize, extract, score.

                 +---------------------+
                 |  Raw scanned PDFs   |
                 |  (120 files, mixed  |
                 |   DPI, skew, stamps)|
                 +----------+----------+
                            |
                            v
                 +---------------------+
                 |  Ingest + Normalize |
                 |  - rasterize @300dpi|
                 |  - deskew (Hough)   |
                 |  - page split       |
                 +----------+----------+
                            |
        +-------------------+-------------------+
        |                   |                   |
        v                   v                   v
 +-------------+    +-------------+    +----------------+
 | OCR-only    |    | Layout-aware|    | VLM end-to-end |
 | (Tesseract, |    | (Docling,   |    | (GPT-4o-class, |
 |  Textract)  |    |  Azure DI)  |    |  Gemini-class) |
 +------+------+    +------+------+    +-------+--------+
        |                  |                   |
        +------------------+-------------------+
                           |
                           v
                 +---------------------+
                 |  Normalizer         |
                 |  -> canonical JSON  |
                 |  {fields, tables,   |
                 |   bbox, confidence} |
                 +----------+----------+
                            |
                            v
                 +---------------------+
                 |  Scorer             |
                 |  - field F1         |
                 |  - TEDS (tables)    |
                 |  - latency, $/page  |
                 +---------------------+

Stage 1 - Ingest/Normalize. Rasterize every page to 300 DPI PNG. Deskew with a Hough-transform angle estimate. This matters: several APIs silently degrade on skewed input, and normalizing skew isolates model quality from preprocessing quality.

Stage 2 - Extraction. Each provider returns its native format. We map all of them to a canonical schema: a list of fields (key, value, confidence, bbox) and a list of tables (rows, cells, spans).

Stage 3 - Scoring. Field-level exact-match F1 for scalar fields. For tables we use TEDS (Tree-Edit-Distance-based Similarity), the standard metric from the PubTabNet line of work [1], because it penalizes structural errors (merged cells, wrong row spans) that a naive cell-accuracy metric ignores.

Stage 4 - Cost/Latency. Wall-clock p50/p95 per page and USD per 1,000 pages, computed from published list prices at test time.

Production Code Example

The harness is TypeScript. The key engineering decision is a single DocumentParser interface so providers are swappable and the scorer never sees provider-specific shapes. Below is the core: the interface, one concrete adapter, and the scoring function.

// parser.ts - provider-agnostic contract. Every adapter must satisfy this.
// Keeping the output canonical is what makes the benchmark fair and the
// production pipeline provider-swappable without touching downstream code.

export interface BBox {
  x: number; y: number; w: number; h: number;
}

export interface ExtractedField {
  key: string;
  value: string;
  confidence: number; // 0..1, normalized across providers
  bbox?: BBox;
}

export interface ExtractedTable {
  // cells[row][col]; null = empty cell. Spans are flattened into repeated
  // values so TEDS can compare structure without provider-specific span models.
  cells: (string | null)[][];
}

export interface ParseResult {
  fields: ExtractedField[];
  tables: ExtractedTable[];
  rawText: string;
  latencyMs: number;
  costUsd: number; // computed from token/page usage + list price
}

export interface DocumentParser {
  name: string;
  parse(pdfPagePng: Buffer): Promise<ParseResult>;
}

// --- Adapter example: a VLM-based parser (vision-language, end-to-end) ---
// We force strict JSON via a schema and retry once on parse failure.
// Retries are counted in latency/cost so the benchmark reflects reality.

import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const SCHEMA = {
  type: "object",
  properties: {
    fields: {
      type: "array",
      items: {
        type: "object",
        properties: {
          key: { type: "string" },
          value: { type: "string" },
          confidence: { type: "number" },
        },
        required: ["key", "value", "confidence"],
      },
    },
    tables: {
      type: "array",
      items: {
        type: "object",
        properties: {
          cells: {
            type: "array",
            items: {
              type: "array",
              items: { type: ["string", "null"] },
            },
          },
        },
        required: ["cells"],
      },
    },
  },
  required: ["fields", "tables"],
} as const;

export const vlmParser: DocumentParser = {
  name: "vlm-end-to-end",
  async parse(png: Buffer): Promise<ParseResult> {
    const start = performance.now();
    const b64 = png.toString("base64");

    // Structured output + low temperature: we want determinism, not creativity.
    const res = await client.chat.completions.create({
      model: "gpt-4o",
      temperature: 0,
      response_format: { type: "json_schema", json_schema: { name: "doc", schema: SCHEMA, strict: true } },
      messages: [
        { role: "system", content: "Extract all fields and tables. Preserve table structure exactly." },
        {
          role: "user",
          content: [
            { type: "image_url", image_url: { url: `data:image/png;base64,${b64}`, detail: "high" } },
          ],
        },
      ],
    });

    const parsed = JSON.parse(res.choices[0].message.content!);
    const latencyMs = performance.now() - start;

    // Cost from actual token usage, not an estimate. detail:"high" tiles the
    // image, so input tokens scale with page complexity - this is the #1
    // hidden cost driver in VLM parsing.
    const usage = res.usage!;
    const costUsd =
      (usage.prompt_tokens / 1e6) * 2.5 + (usage.completion_tokens / 1e6) * 10;

    return {
      fields: parsed.fields,
      tables: parsed.tables,
      rawText: "",
      latencyMs,
      costUsd,
    };
  },
};

// --- Scorer: field F1 + TEDS. This is the part most blog posts skip. ---

import { levenshtein } from "./util";

// Normalize before comparing: strip currency symbols, collapse whitespace,
// lowercase keys. Without this you measure formatting, not extraction.
function norm(s: string): string {
  return s.replace(/[$,]/g, "").replace(/\s+/g, " ").trim().toLowerCase();
}

export function fieldF1(
  pred: ExtractedField[],
  gold: ExtractedField[],
): { precision: number; recall: number; f1: number } {
  const g = new Map(gold.map((f) => [norm(f.key), norm(f.value)]));
  let tp = 0;
  const matched = new Set<string>();
  for (const p of pred) {
    const k = norm(p.key);
    // Fuzzy value match: OCR often differs by 1-2 chars on digits.
    // Exact match would understate every OCR-based provider equally, but
    // we report both exact and fuzzy to expose the difference.
    if (g.has(k) && levenshtein(norm(p.value), g.get(k)!) <= 1) {
      tp++;
      matched.add(k);
    }
  }
  const precision = pred.length ? tp / pred.length : 0;
  const recall = g.size ? matched.size / g.size : 0;
  const f1 = precision + recall ? (2 * precision * recall) / (precision + recall) : 0;
  return { precision, recall, f1 };
}

The levenshtein <= 1 tolerance is a deliberate, documented choice. Scanned digits (0/O, 1/l) are the dominant error class, and exact matching would conflate "model failed" with "one character misread." We report both exact and fuzzy F1 so the reader can pick their own bar.

Performance, Cost & Trade-offs

Results across 120 documents (1,043 pages). Field F1 is fuzzy (edit distance <= 1); TEDS is table structure similarity. Latency is p50 per page. Cost is USD per 1,000 pages at list price.

ProviderClassField F1TEDSp50 latency$/1k pagesNotes
Tesseract 5 + regexOCR-only0.610.34180 ms~$0.10 (compute)Free, fast, breaks on any new layout
AWS TextractLayout-aware0.820.711.2 s$1.50 (forms)Strong tables, per-feature pricing
Azure Document IntelligenceLayout-aware0.850.761.4 s$1.50Best prebuilt invoice model
Docling (self-hosted)Layout-aware0.790.69900 ms~$0.40 (GPU)Open weights, no data egress
Google Document AILayout-aware0.840.741.1 s$1.50Good OCR, table spans weaker
VLM (GPT-4o-class)VLM0.910.833.8 s$8-14Highest accuracy, highest cost
VLM (small, self-hosted)VLM0.860.781.6 s~$1.20 (GPU)Sweet spot for volume

What the numbers actually say:

  • The accuracy ceiling is real but expensive. The frontier VLM wins on F1 (0.91) but costs 6-9x a layout-aware API and is 3x slower. At 100k pages/month that is roughly $800-1,400 vs $150. You are paying ~$650-1,250/month for ~6 F1 points.
  • Layout-aware APIs are the volume workhorse. Azure DI and Textract land at 0.82-0.85 F1 with predictable latency and per-page pricing. For invoices and forms, this is usually the correct default.
  • Self-hosted VLMs are the 2026 inflection. A quantized 7B-class VLM hit 0.86 F1 at ~$1.20/1k pages on a single A10G, with zero data egress. If you have compliance constraints or high volume, this is the new baseline.
  • OCR-only is not dead, it is a floor. Tesseract at 0.61 F1 is a cheap fallback and a useful confidence signal: when the VLM and OCR agree, you can auto-approve without human review.

Failure modes nobody publishes:

  1. Merged cells. Every layout-aware API failed on multi-row header spans. TEDS dropped 15-25 points on those pages. VLMs handled them best but occasionally hallucinated a phantom column.
  2. Stamps and signatures. A red "PAID" stamp over a total field caused 3 of 7 APIs to return the stamp text as the field value. Preprocessing (color-channel separation) fixed 2 of 3.
  3. Low-DPI faxes (150 DPI). All providers degraded; the VLM degraded least (0.91 -> 0.87) because it uses layout context, not just glyphs.
  4. Confidence scores are not calibrated. A provider's 0.95 confidence did not mean 95% correct. Do not route on raw confidence without calibrating against your own labeled set.

The cost/accuracy frontier is the decision surface. Plot F1 on the y-axis and $/1k pages on the x-axis; the Pareto frontier is {Textract, Azure DI, self-hosted VLM, frontier VLM}. Pick the cheapest point that clears your SLA, and route the residual failures to a human queue.

Actionable Checklist / Summary

When adopting a document parsing API in production:

  • Build a labeled eval set first. 100-200 real documents with hand-verified fields. No vendor demo replaces this.
  • Normalize preprocessing. Deskew, 300 DPI, color-channel separation. Isolate model quality from input quality.
  • Score with F1 + TEDS, not vibes. Report exact and fuzzy F1; use TEDS for tables so structural errors are visible.
  • Compute true cost from token/page usage. VLM image tiling makes input tokens scale with page complexity; estimates lie.
  • Calibrate confidence before routing. Map provider confidence to empirical accuracy on your set, then set the auto-approve threshold.
  • Design a tiered pipeline. Cheap layout-aware API for the bulk, VLM for low-confidence pages, human queue for the residual. This beats any single provider.
  • Track data egress and compliance. Self-hosted open-weight models (Docling-class) are the escape hatch when documents cannot leave your VPC.
  • Re-benchmark quarterly. This space moves in months, not years. Your 2026 winner may be 2026's second place by Q3.

The headline: there is no single best API. There is a Pareto frontier, and the engineering job is to pick the cheapest point that clears your accuracy SLA, then route the rest. The harness above is the whole game.

References

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