# Evidence-First PDF Table Extractor (`defenestrator/evidence-first-pdf-table-extractor`) Actor Extract PDF tables to CSV and JSON with page and bounding-box provenance, OCR fallback, and explicit quality flags. - **URL**: https://apify.com/defenestrator/evidence-first-pdf-table-extractor.md - **Developed by:** [Defenestrator](https://apify.com/defenestrator) (community) - **Categories:** Developer tools, AI, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 document processeds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Evidence-First PDF Table Extractor Extract tables from digital and scanned PDFs into full CSV and JSON exports while preserving **where each table came from**. This Actor extracts PDF tables with review-oriented provenance, bounded outputs, explicit quality flags, and transparent pay-per-event pricing. ### Why use it Basic PDF text extractors flatten tables and discard evidence. This Actor produces: - one dataset row per detected table; - full CSV and JSON files in the default key-value store; - source URL, final URL, PDF SHA-256, page number, and parser bounding box where available; - OCR fallback for scanned regions using local CPU models; - explicit structural quality flags and a documented heuristic score; - bounded cells/exports plus spreadsheet-formula escaping in CSV; - an error or no-table row for every document that does not produce tables. No PDF content is sent to remote document-processing services. Docling and RapidOCR run inside the Actor container. ### Input Use `pdfSources` to upload PDFs or provide public PDF URLs. Defaults are deliberately bounded: - 10 documents per run; - 50 pages per PDF; - 25 MB per PDF; - 60-second absolute DNS/download deadline; - 180-second hard conversion deadline; - 3,072 MB isolated conversion-process RSS limit; - 4,096 MB minimum Actor memory, with 768 MB reserved for the parent process; - standard HTTP/HTTPS ports only; - every connection pinned to a validated public IP; private, loopback, link-local, and metadata-network destinations blocked; - HTTPS-to-HTTP redirect downgrades rejected. `ocrMode` values: - `auto` — inspect the PDF text layer, skip OCR when every processed page already has useful text, otherwise OCR bitmap regions; OCR-derived tables are beta and always flagged `ocr_extraction_requires_review`; - `always` — force full-page OCR; - `never` — disable OCR. The requested and effective OCR modes are both reported in output rows. `tableMode: fast` is the cost-controlled default. `accurate` can improve difficult grouped headers and merged cells, but it is materially slower. Neither mode is presented as universally superior; inspect quality flags and provenance for consequential use. ### Output Missing parser bounding boxes, coordinates, or coordinate origins are represented conservatively and flagged with `missing_bbox_provenance`; page-only or origin-less evidence is not presented as complete bounding-box provenance. Every dataset row and JSON control record includes `contractVersion: 1`. Every dataset row also includes a one-based `sourceIndex`. Document and table IDs include the input occurrence, so repeated URLs or different URLs serving identical bytes cannot overwrite one another. Persisted `sourceUrl` and `finalUrl` values are intentionally redacted and are not raw retrievable URLs. Dataset rows use `recordType: table` for extracted tables and `recordType: document` for `no_tables`, `error`, `partial_write`, or `billing_error` outcomes. Every row is validated against a field whitelist and a six-megabyte serialized-size ceiling before persistence. Full exports are stored under: ```text table--001.csv table--001.json document-.md document--manifest.json OUTPUT ```` The dataset includes only a bounded preview; use `csvRecordKey` or `jsonRecordKey` for the complete table. The document manifest is written last in the core persistence sequence. `status: complete` marks fully persisted core output; `status: partial_write` explicitly lists artifacts and dataset rows written before a late storage failure; `status: billing_error` means core output was complete but a configured PPE charge did not fully settle. Confirmed partial charges and the failed event are preserved in the finalized manifest and `OUTPUT`. JSON preserves bounded extracted values. CSV prefixes spreadsheet-formula-like strings with an apostrophe; `outputFlags` and `spreadsheetEscapedCells` report when that happened. ### Quality score `qualityScore` is a transparent structural heuristic, **not model confidence or measured extraction accuracy**. It penalizes empty/single-column tables, duplicate or blank headers, high blank-cell ratios, missing page provenance, and missing/incomplete bounding-box coordinates or coordinate origin. Run summaries therefore use `structurallyUnflaggedTables` and `structurallyFlaggedTables`, not “high quality.” Consumers should validate critical data against the cited PDF page and bounding box. ### Pricing and PPE billing This Actor uses **pay per event + Apify platform usage**. It preflights the user's maximum run budget before conversion and charges custom events only after document artifacts, dataset rows, and the core completion manifest are persisted. Configured events: - `apify-actor-start` — `$0.00005` per allocated memory GB, charged automatically by Apify; - `document-processed` — `$0.010` per successfully persisted document; - `digital-page` — `$0.002` per selected page when OCR is skipped; - `ocr-page` — `$0.010` per selected page when OCR runs. The synthetic `apify-default-dataset-item` event is deliberately disabled to avoid duplicate dataset-output charges. Failed downloads, safety rejections, conversion failures, and OCR page-limit rejections are not charged by these custom events. Because platform usage is passed through, users can still incur small Apify platform-usage charges for failed runs even when no custom event is charged. ### Limitations - OCR is capped at three selected pages per document in this release. Set `maxPagesPerDocument` to `3` or less, split longer scans, or use `ocrMode: never` for digital PDFs. - OCR table extraction is beta. Every OCR-derived table carries `ocr_extraction_requires_review`; clear image tables can still be missed, and detected cells can be misaligned. - Complex nested headers, rotated tables, handwriting, and low-resolution scans may require review. - OCR has been validated on English synthetic and public samples; broader language coverage is not established. - Bounding-box coordinates use the parser's reported coordinate origin. - URL DNS resolution has an absolute deadline; each connection uses the validated public IP directly while TLS verifies the original hostname. Redirects repeat validation and pinning. - PDFium preflight and Docling conversion run in separate spawned process groups. The parent enforces hard wall-clock deadlines, samples process-tree RSS every 250 ms, and kills a group on an observed breach. RSS supervision is not a kernel/cgroup hard cap; a brief spike or container OOM can still precede the next sample. - After Actor storage initialization, Python-level input, document, and fail-fast errors persist `OUTPUT` through an outer `finally`; Actor initialization failure, infrastructure termination, or container OOM can still prevent an application-level finalizer. - Tables, provenance, previews, dataset rows, and exports are bounded; documents exceeding those bounds fail explicitly rather than silently truncating whole tables. - The Actor extracts tables; it does not provide legal, financial, medical, or regulatory interpretation. - This Actor is CPU-oriented and prioritizes inspectable output over speed. ### Example input ```json { "pdfSources": ["https://arxiv.org/pdf/2206.01062"], "ocrMode": "auto", "tableMode": "fast", "maxDocuments": 1, "maxPagesPerDocument": 20, "maxFileSizeMb": 10, "downloadTimeoutSeconds": 60, "documentTimeoutSeconds": 180, "conversionMemoryLimitMb": 3072, "saveDocumentMarkdown": true, "failRunOnDocumentError": false } ``` # Actor input Schema ## `pdfSources` (type: `array`): Upload PDF files or provide public HTTP/HTTPS PDF URLs. Private, loopback, link-local, and cloud-metadata destinations are blocked. ## `ocrMode` (type: `string`): Auto OCRs bitmap regions while retaining embedded PDF text. Always forces full-page OCR. Never disables OCR. OCR table extraction is beta and every OCR-derived table is flagged for review. Effective OCR is limited to three selected pages per document; split longer scans or set the page limit to 3 or less. ## `tableMode` (type: `string`): Fast is the cost-controlled default. Accurate can improve complex/grouped headers but takes materially longer. ## `maxDocuments` (type: `integer`): Hard run cap. Extra supplied documents are not processed. ## `maxPagesPerDocument` (type: `integer`): Processing stops at this page limit for each PDF. If OCR is effective, this value must be 3 or less; digital PDFs with OCR skipped can use the full range. ## `maxFileSizeMb` (type: `integer`): Download and conversion safety limit per PDF. ## `downloadTimeoutSeconds` (type: `integer`): Absolute DNS, connection, redirect, and body download deadline per PDF. ## `documentTimeoutSeconds` (type: `integer`): Hard wall-clock deadline for the isolated Docling process. PDF preflight is separately isolated and capped at the lesser of 30 seconds or this value. ## `conversionMemoryLimitMb` (type: `integer`): PDF preflight and Docling process-tree RSS are sampled every 250 ms and the process group is killed on an observed breach. This is not a kernel/cgroup hard cap. The value must fit within allocated Actor memory while leaving a 768 MB parent-process reserve. ## `saveDocumentMarkdown` (type: `boolean`): Save a bounded full-document Markdown export in the default key-value store. ## `failRunOnDocumentError` (type: `boolean`): Normally each failed PDF produces an explicit error row and the remaining PDFs continue. ## Actor input object example ```json { "pdfSources": [ "https://arxiv.org/pdf/2206.01062" ], "ocrMode": "auto", "tableMode": "fast", "maxDocuments": 10, "maxPagesPerDocument": 50, "maxFileSizeMb": 25, "downloadTimeoutSeconds": 60, "documentTimeoutSeconds": 180, "conversionMemoryLimitMb": 3072, "saveDocumentMarkdown": true, "failRunOnDocumentError": false } ``` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (type: `string`): No description ## `exports` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "pdfSources": [ "https://arxiv.org/pdf/2206.01062" ] }; // Run the Actor and wait for it to finish const run = await client.actor("defenestrator/evidence-first-pdf-table-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "pdfSources": ["https://arxiv.org/pdf/2206.01062"] } # Run the Actor and wait for it to finish run = client.actor("defenestrator/evidence-first-pdf-table-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "pdfSources": [ "https://arxiv.org/pdf/2206.01062" ] }' | apify call defenestrator/evidence-first-pdf-table-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=defenestrator/evidence-first-pdf-table-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Evidence-First PDF Table Extractor", "description": "Extract PDF tables to CSV and JSON with page and bounding-box provenance, OCR fallback, and explicit quality flags.", "version": "0.0", "x-build-id": "pCVl1eGQvyE8yVMPL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/defenestrator~evidence-first-pdf-table-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-defenestrator-evidence-first-pdf-table-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/defenestrator~evidence-first-pdf-table-extractor/runs": { "post": { "operationId": "runs-sync-defenestrator-evidence-first-pdf-table-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/defenestrator~evidence-first-pdf-table-extractor/run-sync": { "post": { "operationId": "run-sync-defenestrator-evidence-first-pdf-table-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "pdfSources" ], "properties": { "pdfSources": { "title": "PDF files or URLs", "minItems": 1, "maxItems": 10, "type": "array", "description": "Upload PDF files or provide public HTTP/HTTPS PDF URLs. Private, loopback, link-local, and cloud-metadata destinations are blocked.", "items": { "type": "string", "minLength": 8, "maxLength": 4096 } }, "ocrMode": { "title": "OCR mode", "enum": [ "auto", "always", "never" ], "type": "string", "description": "Auto OCRs bitmap regions while retaining embedded PDF text. Always forces full-page OCR. Never disables OCR. OCR table extraction is beta and every OCR-derived table is flagged for review. Effective OCR is limited to three selected pages per document; split longer scans or set the page limit to 3 or less.", "default": "auto" }, "tableMode": { "title": "Table quality mode", "enum": [ "fast", "accurate" ], "type": "string", "description": "Fast is the cost-controlled default. Accurate can improve complex/grouped headers but takes materially longer.", "default": "fast" }, "maxDocuments": { "title": "Maximum documents", "minimum": 1, "maximum": 10, "type": "integer", "description": "Hard run cap. Extra supplied documents are not processed.", "default": 10 }, "maxPagesPerDocument": { "title": "Maximum pages per document", "minimum": 1, "maximum": 200, "type": "integer", "description": "Processing stops at this page limit for each PDF. If OCR is effective, this value must be 3 or less; digital PDFs with OCR skipped can use the full range.", "default": 50 }, "maxFileSizeMb": { "title": "Maximum PDF size (MB)", "minimum": 1, "maximum": 100, "type": "integer", "description": "Download and conversion safety limit per PDF.", "default": 25 }, "downloadTimeoutSeconds": { "title": "PDF download deadline", "minimum": 15, "maximum": 120, "type": "integer", "description": "Absolute DNS, connection, redirect, and body download deadline per PDF.", "default": 60 }, "documentTimeoutSeconds": { "title": "Per-document conversion deadline", "minimum": 30, "maximum": 600, "type": "integer", "description": "Hard wall-clock deadline for the isolated Docling process. PDF preflight is separately isolated and capped at the lesser of 30 seconds or this value.", "default": 180 }, "conversionMemoryLimitMb": { "title": "Supervised process-tree RSS ceiling (MB)", "minimum": 1024, "maximum": 6144, "type": "integer", "description": "PDF preflight and Docling process-tree RSS are sampled every 250 ms and the process group is killed on an observed breach. This is not a kernel/cgroup hard cap. The value must fit within allocated Actor memory while leaving a 768 MB parent-process reserve.", "default": 3072 }, "saveDocumentMarkdown": { "title": "Save document Markdown", "type": "boolean", "description": "Save a bounded full-document Markdown export in the default key-value store.", "default": true }, "failRunOnDocumentError": { "title": "Fail run when any document fails", "type": "boolean", "description": "Normally each failed PDF produces an explicit error row and the remaining PDFs continue.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```