OCR & Document Intelligence

Extract text from images, analyze document layouts, process invoices, redact sensitive data, and batch-process documents at scale. FOTOhub's OCR pipeline combines high-accuracy text detection with intelligent document understanding for 40+ languages.

Feature	Endpoint	Credits	Description
OCR	`POST /v1/ai/document/ocr`	1	Detect and extract text from images
Redact	`POST /v1/ai/document/redact`	2	Redact PII and sensitive text for compliance
PDF to Images	`POST /v1/ai/document/pdf-to-images`	1/page	Convert PDF pages to individual images
Batch OCR	`POST /v1/ai/document/batch`	1/image	Process up to 100 images in a single request
Export	`POST /v1/ai/document/export`	Free	Export prior OCR results as JSON, CSV, or text
Invoice	`POST /v1/ai/document/invoice`	3	Extract structured data from invoices/receipts
Analyze	`POST /v1/ai/document/analyze`	2	Full document analysis with layout detection

Supported formats: JPEG, PNG, TIFF, WebP, BMP, PDF (max 10MB per file, max 100 pages for batch).

Authentication

All endpoints require a valid API key passed in the Authorization header:

Authorization: Bearer YOUR_API_KEY

Base URL: https://apis.fotohub.app

OCR — Text Detection

Fast, high-accuracy text extraction from any image. Returns detected text with per-line confidence scores, bounding boxes, and detected language.

Endpoint

POST /v1/ai/document/ocr

Billing: 1 credit per image

Parameters

Parameter	Type	Required	Default	Description
`image`	string	Yes	—	Base64-encoded image or public URL. Max 10MB.
`language_hints`	array	No	auto-detect	ISO 639-1 codes to hint preferred languages, e.g. `["en", "pl", "de"]`.
`include_bounding_boxes`	boolean	No	`false`	Return pixel coordinates for each text block.
`output_format`	string	No	`"structured"`	`"structured"` (lines + metadata) or `"plain"` (raw text only).

Response

json

{
  "text": "FOTOhub Invoice #FH-2026-0147\nDate: 2026-07-22\nBill To: Acme Corp\nTotal: 1,249.00 PLN",
  "lines": [
    {
      "text": "FOTOhub Invoice #FH-2026-0147",
      "confidence": 99.7,
      "bounding_box": { "x": 42, "y": 18, "width": 380, "height": 32 }
    },
    {
      "text": "Date: 2026-07-22",
      "confidence": 99.4,
      "bounding_box": { "x": 42, "y": 62, "width": 180, "height": 24 }
    },
    {
      "text": "Bill To: Acme Corp",
      "confidence": 98.9,
      "bounding_box": { "x": 42, "y": 94, "width": 210, "height": 24 }
    },
    {
      "text": "Total: 1,249.00 PLN",
      "confidence": 99.6,
      "bounding_box": { "x": 42, "y": 126, "width": 220, "height": 24 }
    }
  ],
  "detected_language": "en",
  "total_lines": 4,
  "credits_used": 1
}

Examples

PythonTypeScriptcURL

python

import base64
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

# From a local file
with open("receipt.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

result = client.document.ocr(image=image_b64)
print(result.text)

# From a URL
result = client.document.ocr(
    image="https://example.com/document.png",
    language_hints=["en", "pl"],
    include_bounding_boxes=True
)

for line in result.lines:
    print(f"[{line.confidence:.1f}%] {line.text}")

typescript

import { FotoHub } from "fotohub";
import { readFileSync } from "fs";

const client = new FotoHub({ apiKey: "fh_live_..." });

// From a local file
const imageB64 = readFileSync("receipt.jpg").toString("base64");

const result = await client.document.ocr({
  image: imageB64,
  languageHints: ["en", "pl"],
  includeBoundingBoxes: true,
});

console.log(result.text);

for (const line of result.lines) {
  console.log(`[${line.confidence.toFixed(1)}%] ${line.text}`);
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/ocr" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "BASE64_ENCODED_IMAGE...",
    "language_hints": ["en", "pl"],
    "include_bounding_boxes": true
  }'

Redact — PII Removal

Automatically detect and mask personally identifiable information (PII) in documents. Returns a redacted image with sensitive text replaced by black bars, plus a report of what was detected.

Endpoint

POST /v1/ai/document/redact

Billing: 2 credits per image

Parameters

Parameter	Type	Required	Default	Description
`image`	string	Yes	—	Base64-encoded image or public URL. Max 10MB.
`redaction_types`	array	No	all types	Specific PII types to redact. See table below.
`mask_style`	string	No	`"black_bar"`	`"black_bar"`, `"blur"`, or `"pixelate"`.
`return_detections`	boolean	No	`true`	Include list of detected PII items in response.

Redaction Types

Type	Detects
`NAME`	Full names, first/last names
`EMAIL`	Email addresses
`PHONE`	Phone numbers (international formats)
`ADDRESS`	Street addresses, postal codes, cities
`SSN`	Social security numbers, PESEL, national IDs
`CREDIT_CARD`	Credit/debit card numbers
`DATE_OF_BIRTH`	Birth dates
`BANK_ACCOUNT`	IBAN, account numbers
`LICENSE_PLATE`	Vehicle registration numbers
`PASSPORT`	Passport numbers
`IP_ADDRESS`	IPv4 and IPv6 addresses
`SIGNATURE`	Handwritten signatures (visual masking)

Response

json

{
  "redacted_image": "https://s1.fotohub.app/storage/v1/object/public/generations/redact_abc123.png",
  "detections": [
    { "type": "NAME", "value": "Jan Kowalski", "confidence": 97.3, "masked": true },
    { "type": "EMAIL", "value": "[email protected]", "confidence": 99.1, "masked": true },
    { "type": "PHONE", "value": "+48 600 123 456", "confidence": 98.5, "masked": true },
    { "type": "ADDRESS", "value": "ul. Marszalkowska 1, 00-001 Warszawa", "confidence": 96.8, "masked": true }
  ],
  "total_redactions": 4,
  "credits_used": 2
}

Examples

PythonTypeScriptcURL

python

import base64
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

with open("contract.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

result = client.document.redact(
    image=image_b64,
    redaction_types=["NAME", "EMAIL", "PHONE", "SSN"],
    mask_style="black_bar"
)

print(f"Redacted {result.total_redactions} items")
print(f"Redacted image: {result.redacted_image}")

for detection in result.detections:
    print(f"  [{detection.type}] {detection.value}")

typescript

import { FotoHub } from "fotohub";
import { readFileSync } from "fs";

const client = new FotoHub({ apiKey: "fh_live_..." });

const imageB64 = readFileSync("contract.png").toString("base64");

const result = await client.document.redact({
  image: imageB64,
  redactionTypes: ["NAME", "EMAIL", "PHONE", "SSN"],
  maskStyle: "black_bar",
});

console.log(`Redacted ${result.totalRedactions} items`);
console.log(`Redacted image: ${result.redactedImage}`);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/redact" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "BASE64_ENCODED_IMAGE...",
    "redaction_types": ["NAME", "EMAIL", "PHONE", "SSN"],
    "mask_style": "black_bar"
  }'

GDPR Compliance

Redacted images are stored temporarily (24 hours) then permanently deleted. The original image is never stored. For GDPR data processing agreements, contact [email protected].

PDF to Images

Convert PDF pages to individual high-resolution images for downstream OCR processing or display.

Endpoint

POST /v1/ai/document/pdf-to-images

Billing: 1 credit per page converted

Parameters

Parameter	Type	Required	Default	Description
`document`	string	Yes	—	Base64-encoded PDF. Max 10MB.
`pages`	string	No	`"all"`	Page range: `"all"`, `"1-5"`, `"1,3,7"`, or `"1-3,5,8-10"`.
`dpi`	integer	No	`200`	Output resolution: `72`, `150`, `200`, or `300`.
`format`	string	No	`"png"`	Output format: `"png"` or `"jpeg"`.

Response

json

{
  "images": [
    {
      "page": 1,
      "url": "https://s1.fotohub.app/storage/v1/object/public/generations/pdf_p1_abc123.png",
      "width": 1654,
      "height": 2339
    },
    {
      "page": 2,
      "url": "https://s1.fotohub.app/storage/v1/object/public/generations/pdf_p2_abc123.png",
      "width": 1654,
      "height": 2339
    }
  ],
  "total_pages": 2,
  "credits_used": 2
}

Examples

PythonTypeScriptcURL

python

import base64
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

with open("report.pdf", "rb") as f:
    pdf_b64 = base64.b64encode(f.read()).decode()

result = client.document.pdf_to_images(
    document=pdf_b64,
    pages="1-5",
    dpi=300,
    format="png"
)

for page in result.images:
    print(f"Page {page.page}: {page.url}")

typescript

import { FotoHub } from "fotohub";
import { readFileSync } from "fs";

const client = new FotoHub({ apiKey: "fh_live_..." });

const pdfB64 = readFileSync("report.pdf").toString("base64");

const result = await client.document.pdfToImages({
  document: pdfB64,
  pages: "1-5",
  dpi: 300,
  format: "png",
});

for (const page of result.images) {
  console.log(`Page ${page.page}: ${page.url}`);
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/pdf-to-images" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "document": "BASE64_ENCODED_PDF...",
    "pages": "1-5",
    "dpi": 300,
    "format": "png"
  }'

Batch OCR

Process multiple images in a single request. Ideal for multi-page documents, photo archives, or bulk digitization workflows. Up to 100 images per batch.

Endpoint

POST /v1/ai/document/batch

Billing: 1 credit per image in the batch

Parameters

Parameter	Type	Required	Default	Description
`images`	array	Yes	—	Array of base64-encoded images or URLs. Max 100 items, 10MB each.
`language_hints`	array	No	auto-detect	ISO 639-1 language codes applied to all images.
`include_bounding_boxes`	boolean	No	`false`	Return bounding boxes for each detected line.

Response

json

{
  "results": [
    {
      "index": 0,
      "text": "Page 1 content here...",
      "lines": [
        { "text": "Page 1 content here...", "confidence": 99.2 }
      ],
      "detected_language": "en",
      "status": "success"
    },
    {
      "index": 1,
      "text": "Page 2 content here...",
      "lines": [
        { "text": "Page 2 content here...", "confidence": 98.8 }
      ],
      "detected_language": "en",
      "status": "success"
    }
  ],
  "total_processed": 2,
  "total_failed": 0,
  "credits_used": 2
}

Examples

PythonTypeScriptcURL

python

import base64
import glob
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

# Batch process all images in a folder
images = []
for path in glob.glob("scanned_pages/*.jpg"):
    with open(path, "rb") as f:
        images.append(base64.b64encode(f.read()).decode())

result = client.document.batch(
    images=images,
    language_hints=["en"]
)

print(f"Processed {result.total_processed} pages")
for page in result.results:
    print(f"  Page {page.index + 1}: {page.text[:80]}...")

typescript

import { FotoHub } from "fotohub";
import { readFileSync, readdirSync } from "fs";
import { join } from "path";

const client = new FotoHub({ apiKey: "fh_live_..." });

const dir = "scanned_pages";
const images = readdirSync(dir)
  .filter((f) => f.endsWith(".jpg"))
  .map((f) => readFileSync(join(dir, f)).toString("base64"));

const result = await client.document.batch({
  images,
  languageHints: ["en"],
});

console.log(`Processed ${result.totalProcessed} pages`);
for (const page of result.results) {
  console.log(`  Page ${page.index + 1}: ${page.text.slice(0, 80)}...`);
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/batch" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "images": [
      "BASE64_IMAGE_1...",
      "BASE64_IMAGE_2...",
      "BASE64_IMAGE_3..."
    ],
    "language_hints": ["en"]
  }'

Export Results

Export OCR results from a previous request in JSON, CSV, or plain text format. This endpoint is free when referencing a prior OCR job.

Endpoint

POST /v1/ai/document/export

Billing: Free (requires a valid job_id from a prior OCR/analyze call)

Parameters

Parameter	Type	Required	Default	Description
`job_id`	string	Yes	—	Job ID from a previous OCR, batch, or analyze request.
`format`	string	No	`"json"`	Export format: `"json"`, `"csv"`, or `"text"`.
`include_confidence`	boolean	No	`true`	Include confidence scores in export.
`include_bounding_boxes`	boolean	No	`false`	Include bounding box coordinates.

Response

For format: "json":

json

{
  "export_url": "https://s1.fotohub.app/storage/v1/object/public/exports/ocr_export_abc123.json",
  "format": "json",
  "size_bytes": 4280,
  "expires_at": "2026-07-23T12:00:00Z",
  "credits_used": 0
}

For format: "csv":

json

{
  "export_url": "https://s1.fotohub.app/storage/v1/object/public/exports/ocr_export_abc123.csv",
  "format": "csv",
  "size_bytes": 2140,
  "expires_at": "2026-07-23T12:00:00Z",
  "credits_used": 0
}

Examples

PythonTypeScriptcURL

python

from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

# First, run OCR
ocr_result = client.document.ocr(image=image_b64)

# Then export as CSV
export = client.document.export(
    job_id=ocr_result.job_id,
    format="csv",
    include_confidence=True
)

print(f"Download: {export.export_url}")

typescript

import { FotoHub } from "fotohub";

const client = new FotoHub({ apiKey: "fh_live_..." });

// First, run OCR
const ocrResult = await client.document.ocr({ image: imageB64 });

// Then export as CSV
const exportResult = await client.document.export({
  jobId: ocrResult.jobId,
  format: "csv",
  includeConfidence: true,
});

console.log(`Download: ${exportResult.exportUrl}`);

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/export" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "job_id": "ocr_job_abc123",
    "format": "csv",
    "include_confidence": true
  }'

Invoice Extraction

Extract structured data from invoices and receipts. Returns vendor details, dates, totals, tax breakdowns, line items, and payment information in a normalized schema.

Endpoint

POST /v1/ai/document/invoice

Billing: 3 credits per document

Parameters

Parameter	Type	Required	Default	Description
`image`	string	Yes	—	Base64-encoded invoice/receipt image or URL. Max 10MB.
`extract_line_items`	boolean	No	`true`	Extract individual line items with quantities and prices.
`currency_hint`	string	No	auto-detect	ISO 4217 currency code hint, e.g. `"PLN"`, `"USD"`, `"EUR"`.

Response Schema

json

{
  "invoice": {
    "vendor": {
      "name": "FOTOhub sp. z o.o.",
      "address": "ul. Mokotowska 15, 00-640 Warszawa",
      "tax_id": "PL5213900482",
      "confidence": 97.5
    },
    "customer": {
      "name": "Acme Corp Sp. z o.o.",
      "address": "ul. Nowy Swiat 42, 00-363 Warszawa",
      "tax_id": "PL1234567890",
      "confidence": 96.8
    },
    "details": {
      "invoice_number": "FH-2026/07/0147",
      "issue_date": "2026-07-22",
      "due_date": "2026-08-05",
      "payment_method": "bank_transfer",
      "bank_account": "PL 12 1234 5678 9012 3456 7890 1234"
    },
    "totals": {
      "subtotal": { "amount": 1249.00, "currency": "PLN" },
      "tax": { "amount": 287.27, "currency": "PLN", "rate": "23%" },
      "total": { "amount": 1536.27, "currency": "PLN" },
      "paid": { "amount": 0.00, "currency": "PLN" },
      "due": { "amount": 1536.27, "currency": "PLN" }
    },
    "line_items": [
      {
        "description": "AI Credits Bundle (5000)",
        "quantity": 1,
        "unit_price": { "amount": 999.00, "currency": "PLN" },
        "total": { "amount": 999.00, "currency": "PLN" },
        "confidence": 98.3
      },
      {
        "description": "Cloud Storage 500GB (monthly)",
        "quantity": 1,
        "unit_price": { "amount": 250.00, "currency": "PLN" },
        "total": { "amount": 250.00, "currency": "PLN" },
        "confidence": 97.9
      }
    ]
  },
  "document_type": "invoice",
  "detected_language": "pl",
  "overall_confidence": 97.6,
  "credits_used": 3
}

Examples

PythonTypeScriptcURL

python

import base64
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

with open("invoice.pdf", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

result = client.document.invoice(
    image=image_b64,
    extract_line_items=True,
    currency_hint="PLN"
)

inv = result.invoice
print(f"Vendor: {inv.vendor.name}")
print(f"Invoice #: {inv.details.invoice_number}")
print(f"Total: {inv.totals.total.amount} {inv.totals.total.currency}")
print(f"Due date: {inv.details.due_date}")

print("\nLine items:")
for item in inv.line_items:
    print(f"  {item.description}: {item.total.amount} {item.total.currency}")

typescript

import { FotoHub } from "fotohub";
import { readFileSync } from "fs";

const client = new FotoHub({ apiKey: "fh_live_..." });

const imageB64 = readFileSync("invoice.pdf").toString("base64");

const result = await client.document.invoice({
  image: imageB64,
  extractLineItems: true,
  currencyHint: "PLN",
});

const inv = result.invoice;
console.log(`Vendor: ${inv.vendor.name}`);
console.log(`Invoice #: ${inv.details.invoiceNumber}`);
console.log(`Total: ${inv.totals.total.amount} ${inv.totals.total.currency}`);

for (const item of inv.lineItems) {
  console.log(`  ${item.description}: ${item.total.amount} ${item.total.currency}`);
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/invoice" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "BASE64_ENCODED_INVOICE...",
    "extract_line_items": true,
    "currency_hint": "PLN"
  }'

Analyze — Full Document Analysis

Comprehensive document analysis with layout detection, table extraction, form field recognition, and signature detection. Best for complex, multi-section documents.

Endpoint

POST /v1/ai/document/analyze

Billing: 2 credits per page

Parameters

Parameter	Type	Required	Default	Description
`image`	string	Yes	—	Base64-encoded document image or URL. Max 10MB.
`features`	array	No	`["TEXT", "TABLES", "FORMS"]`	Features to extract. Options: `"TEXT"`, `"TABLES"`, `"FORMS"`, `"SIGNATURES"`, `"LAYOUT"`.
`language_hints`	array	No	auto-detect	ISO 639-1 codes to prioritize specific languages.

Response

json

{
  "text": "Complete extracted text from the document...",
  "lines": [
    { "text": "Section 1: Agreement Terms", "confidence": 99.4, "type": "HEADING" },
    { "text": "This agreement is entered into...", "confidence": 98.7, "type": "PARAGRAPH" }
  ],
  "layout": {
    "sections": [
      { "type": "HEADER", "bounding_box": { "x": 0, "y": 0, "width": 800, "height": 60 } },
      { "type": "TABLE", "bounding_box": { "x": 40, "y": 200, "width": 720, "height": 300 } },
      { "type": "SIGNATURE_BLOCK", "bounding_box": { "x": 40, "y": 900, "width": 300, "height": 80 } }
    ]
  },
  "tables": [
    {
      "rows": [
        ["Service", "Period", "Amount"],
        ["AI Credits", "July 2026", "999.00 PLN"],
        ["Storage", "July 2026", "250.00 PLN"]
      ],
      "headers_detected": true,
      "confidence": 97.8
    }
  ],
  "forms": [
    { "key": "Company Name", "value": "FOTOhub sp. z o.o.", "confidence": 98.5 },
    { "key": "Registration Number", "value": "KRS 0000912345", "confidence": 97.2 }
  ],
  "signatures": [
    {
      "detected": true,
      "bounding_box": { "x": 50, "y": 910, "width": 180, "height": 60 },
      "confidence": 94.3
    }
  ],
  "total_blocks": 87,
  "credits_used": 2
}

Examples

PythonTypeScriptcURL

python

import base64
from fotohub import FotoHub

client = FotoHub(api_key="fh_live_...")

with open("contract.pdf", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

result = client.document.analyze(
    image=image_b64,
    features=["TEXT", "TABLES", "FORMS", "SIGNATURES", "LAYOUT"]
)

# Full text
print(result.text)

# Tables
for table in result.tables:
    print("\n--- Table ---")
    for row in table.rows:
        print(" | ".join(row))

# Form fields
print("\n--- Form Fields ---")
for field in result.forms:
    print(f"{field.key}: {field.value}")

# Signatures
for sig in result.signatures:
    if sig.detected:
        print(f"Signature found at ({sig.bounding_box.x}, {sig.bounding_box.y})")

typescript

import { FotoHub } from "fotohub";
import { readFileSync } from "fs";

const client = new FotoHub({ apiKey: "fh_live_..." });

const imageB64 = readFileSync("contract.pdf").toString("base64");

const result = await client.document.analyze({
  image: imageB64,
  features: ["TEXT", "TABLES", "FORMS", "SIGNATURES", "LAYOUT"],
});

// Tables
for (const table of result.tables) {
  console.log("\n--- Table ---");
  for (const row of table.rows) {
    console.log(row.join(" | "));
  }
}

// Form fields
for (const field of result.forms) {
  console.log(`${field.key}: ${field.value}`);
}

bash

curl -X POST "https://apis.fotohub.app/v1/ai/document/analyze" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "BASE64_ENCODED_DOCUMENT...",
    "features": ["TEXT", "TABLES", "FORMS", "SIGNATURES", "LAYOUT"]
  }'

Supported Languages

FOTOhub OCR supports 40+ languages for text detection. Language detection is automatic, but you can provide hints for improved accuracy on multilingual documents.

Language	Code	Language	Code	Language	Code
English	`en`	Polish	`pl`	German	`de`
French	`fr`	Spanish	`es`	Italian	`it`
Portuguese	`pt`	Dutch	`nl`	Swedish	`sv`
Norwegian	`no`	Danish	`da`	Finnish	`fi`
Czech	`cs`	Slovak	`sk`	Hungarian	`hu`
Romanian	`ro`	Bulgarian	`bg`	Croatian	`hr`
Serbian	`sr`	Slovenian	`sl`	Ukrainian	`uk`
Russian	`ru`	Greek	`el`	Turkish	`tr`
Arabic	`ar`	Hebrew	`he`	Hindi	`hi`
Thai	`th`	Vietnamese	`vi`	Indonesian	`id`
Malay	`ms`	Japanese	`ja`	Chinese (Simplified)	`zh-CN`
Chinese (Traditional)	`zh-TW`	Korean	`ko`	Bengali	`bn`
Tamil	`ta`	Telugu	`te`	Marathi	`mr`
Gujarati	`gu`	Kannada	`kn`	Malayalam	`ml`
Punjabi	`pa`	Urdu	`ur`

Pricing

Operation	Credits	Provider Cost	Best For
OCR (text detection)	1	$0.0015/page	Simple text extraction, receipts, labels
Redact (PII masking)	2	$0.003/page	GDPR compliance, data anonymization
PDF to Images	1/page	$0.001/page	PDF preprocessing for OCR pipeline
Batch OCR	1/image	$0.0015/image	Bulk digitization, archives
Export	Free	—	Downloading results in structured formats
Invoice extraction	3	$0.01/page	Accounting automation, expense tracking
Document analysis	2	$0.005/page	Complex documents, contracts, forms

Volume Discounts

Processing over 1,000 documents per month? Contact [email protected] for volume pricing with up to 40% discounts.

Use Cases

Document Digitization

Convert paper archives, scanned books, and legacy documents into searchable, indexed text. Use batch OCR for high-volume processing with export to CSV for database import.

Compliance & Data Privacy

Automatically redact PII from documents before sharing or archiving. Detect names, emails, phone numbers, SSNs, and addresses. Meets GDPR Article 17 (right to erasure) and Article 25 (data protection by design) requirements.

Financial Data Extraction

Process invoices, receipts, and expense reports at scale. Extract vendor names, amounts, dates, tax IDs, and line items into structured JSON for direct integration with accounting systems (SAP, QuickBooks, Xero).

Accessibility

Convert image-based documents into machine-readable text for screen readers and assistive technology. Supports 40+ languages for international accessibility compliance (WCAG 2.1, Section 508).

Form Processing

Digitize paper forms, applications, and surveys. The analyze endpoint detects form fields (key-value pairs) and checkbox states, enabling automated data entry workflows.

Contract Analysis

Extract key terms, signature blocks, dates, and party names from contracts. Use layout detection to identify sections, tables of terms, and signature positions.

Error Responses

Status	Code	Description
400	`invalid_image`	Image could not be decoded. Verify base64 encoding or URL accessibility.
400	`unsupported_format`	File format not supported. Use JPEG, PNG, TIFF, WebP, BMP, or PDF.
400	`document_too_large`	File exceeds 10MB limit. Compress or split the document.
400	`batch_limit_exceeded`	Batch contains more than 100 images. Split into smaller batches.
400	`invalid_job_id`	Export job ID not found or expired. Job results expire after 24 hours.
400	`invalid_page_range`	PDF page range is invalid or exceeds document page count.
402	`insufficient_credits`	Not enough credits for this operation. Top up at fotohub.app/billing.
413	`payload_too_large`	Request body exceeds maximum size. Reduce image count or resolution.
429	`rate_limited`	Too many requests. Default: 60 requests/minute. See Rate Limits.
500	`ocr_failed`	Internal OCR engine error. Retry after a few seconds.
503	`service_unavailable`	OCR service temporarily unavailable. Check status.fotohub.app.

Rate Limits

Tier	Requests/min	Batch size	Concurrent
Free	10	10 images	2
Pro	60	50 images	10
Business	200	100 images	25
Enterprise	Custom	Custom	Custom

See Rate Limits for full details on tier-specific limits and retry strategies.

OCR & Document Intelligence ​

OCR — Text Detection ​

Endpoint ​

Parameters ​

Response ​

Examples ​

Redact — PII Removal ​

Endpoint ​

Parameters ​

Redaction Types ​

Response ​

Examples ​

PDF to Images ​

Endpoint ​

Parameters ​

Response ​

Examples ​

Batch OCR ​

Endpoint ​

Parameters ​

Response ​

Examples ​

Export Results ​

Endpoint ​

Parameters ​

Response ​

Examples ​

Invoice Extraction ​

Endpoint ​

Parameters ​

Response Schema ​

Examples ​

Analyze — Full Document Analysis ​

Endpoint ​

Parameters ​

Response ​

Examples ​

Supported Languages ​

Pricing ​

Use Cases ​

Document Digitization ​

Compliance & Data Privacy ​

Financial Data Extraction ​

Accessibility ​

Form Processing ​

Contract Analysis ​

Error Responses ​

Rate Limits ​

OCR & Document Intelligence

OCR — Text Detection

Endpoint

Parameters

Response

Examples

Redact — PII Removal

Endpoint

Parameters

Redaction Types

Response

Examples

PDF to Images

Endpoint

Parameters

Response

Examples

Batch OCR

Endpoint

Parameters

Response

Examples

Export Results

Endpoint

Parameters

Response

Examples

Invoice Extraction

Endpoint

Parameters

Response Schema

Examples

Analyze — Full Document Analysis

Endpoint

Parameters

Response

Examples

Supported Languages

Pricing

Use Cases

Document Digitization

Compliance & Data Privacy

Financial Data Extraction

Accessibility

Form Processing

Contract Analysis

Error Responses

Rate Limits