# Pdf Court Case Extractor (`getdataforme/pdf-court-case-extractor`) Actor The Pdf Court Case Extractor efficiently extracts detailed court case data from PDFs, ideal for legal professionals and researchers. It offers customizable searches, enhanced text extraction via OpenAI, scalable processing, and cloud storage integration, all through a user-friendly interface. - **URL**: https://apify.com/getdataforme/pdf-court-case-extractor.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Pdf Court Case Extractor The Pdf Court Case Extractor is a powerful tool designed to streamline the extraction of court case information from PDF documents. This Actor is ideal for legal professionals, researchers, and analysts who need to efficiently gather and analyze court case data across various counties. ### Features - **Comprehensive Data Extraction**: Extracts detailed information from court case PDFs, including case numbers, descriptions, parties involved, and more. - **Customizable Search**: Filter searches by county, case type, and date range to target specific datasets. - **Enhanced Text Extraction**: Utilizes OpenAI's API for improved text recognition, with a regex fallback option. - **Scalable Processing**: Capable of processing up to 100 documents per run. - **Cloud Storage Integration**: Option to store extracted data in a specified Google Cloud Storage bucket. - **User-Friendly Interface**: Simple setup and execution through the Apify platform. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------------|---------|----------|-----------------------------------------------------------------------------|------------------------| | county | string | Yes | County name to search for court cases | "Orange" | | limit | integer | No | Maximum number of documents to process (1-100) | 10 | | case_type | string | No | Filter by case type | "CA - Auto Negligence" | | date_start | string | No | Start date for filed cases (YYYY-MM-DD) | "2025-01-01" | | date_end | string | No | End date for filed cases (YYYY-MM-DD) | "2025-12-31" | | openai_api_key | string | No | OpenAI API key for enhanced text extraction | | | gcs_bucket_name | string | No | Google Cloud Storage bucket name | "courts-bucket" | ### Example Usage #### Example Input JSON ```json { "county": "Orange", "limit": 10, "case_type": "CA - Auto Negligence", "date_start": "2025-01-01", "date_end": "2025-12-31", "openai_api_key": "your-openai-api-key", "gcs_bucket_name": "courts-bucket" } ```` #### Example Output JSON ```json [ { "case_number": "227383417", "description": "This is an action for damages that exceed Fifty Thousand Dollars ($50,000.00), exclusive of interest, costs and attorneys fees.", "location": "ORANGE COUNTY, FLORIDA", "ucn": "", "case_type": "Civil", "status": "Pending", "judge_name": "", "filed_date": "07/16/2025", "incident_date": "07/28/2024", "parties": [ { "name": "ROBERT E. LAWRIE", "type": "Plaintiff", "attorney": "Matthew Scott Hochstein", "phone": "(689) 285-6976", "email": "MHochstein@forthepeople.com", "address": "20 N. Orange Avenue, Suite 1600, Orlando, FL 32801" }, { "name": "FLORIDA DEPARTMENT OF FINACIAL SERVICES", "type": "Defendant", "attorney": "", "phone": "", "email": "", "address": "" } ], "documents": [ { "date": "07/15/2025", "description": "COMPLAINT", "pages": "", "doc_link": "", "path": "" } ], "actor-id": "", "county": "orangecounty", "court-id": "", "crawled_date": "2025-07-17T16:35:10.352586", "source_collection": "demo_case_documents", "original_document_id": "6878d5628c05ac301a95a22b" } ] ``` ### Use Cases - **Legal Research**: Quickly gather case details for legal analysis. - **Market Research**: Analyze trends in legal cases across different regions. - **Competitive Intelligence**: Monitor legal activities involving competitors. - **Content Aggregation**: Compile case data for publication or reporting. - **Academic Research**: Support studies in legal trends and case law analysis. - **Business Automation**: Automate the collection of legal documents for business processes. ### Installation and Usage 1. Search for "Pdf Court Case Extractor" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array containing detailed information about each court case, including fields such as `case_number`, `description`, `location`, `case_type`, `status`, `filed_date`, `parties`, and `documents`. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `county` (type: `string`): County name to search for court cases (e.g., 'Orange', 'Miami-Dade') ## `limit` (type: `integer`): Maximum number of documents to process ## `case_type` (type: `string`): Filter by case type (e.g., 'CA - Auto Negligence') ## `date_start` (type: `string`): Start date for filed cases (YYYY-MM-DD) ## `date_end` (type: `string`): End date for filed cases (YYYY-MM-DD) ## `openai_api_key` (type: `string`): OpenAI API key for enhanced text extraction (optional - will use regex fallback if not provided) ## `gcs_bucket_name` (type: `string`): Google Cloud Storage bucket name (optional - defaults to 'courts-bucket') ## Actor input object example ```json { "county": "Orange", "limit": 10, "case_type": "CA - Auto Negligence", "date_start": "2025-01-01", "date_end": "2025-12-31", "gcs_bucket_name": "courts-bucket" } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/pdf-court-case-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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/pdf-court-case-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 '{}' | apify call getdataforme/pdf-court-case-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/pdf-court-case-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pdf Court Case Extractor", "description": "The Pdf Court Case Extractor efficiently extracts detailed court case data from PDFs, ideal for legal professionals and researchers. It offers customizable searches, enhanced text extraction via OpenAI, scalable processing, and cloud storage integration, all through a user-friendly interface.", "version": "1.0", "x-build-id": "kik8VO1fNVoUMFw8V" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~pdf-court-case-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-pdf-court-case-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~pdf-court-case-extractor/runs": { "post": { "operationId": "runs-sync-getdataforme-pdf-court-case-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~pdf-court-case-extractor/run-sync": { "post": { "operationId": "run-sync-getdataforme-pdf-court-case-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "county" ], "properties": { "county": { "title": "County", "type": "string", "description": "County name to search for court cases (e.g., 'Orange', 'Miami-Dade')" }, "limit": { "title": "Document Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of documents to process", "default": 10 }, "case_type": { "title": "Case Type Filter", "type": "string", "description": "Filter by case type (e.g., 'CA - Auto Negligence')" }, "date_start": { "title": "Start Date", "type": "string", "description": "Start date for filed cases (YYYY-MM-DD)" }, "date_end": { "title": "End Date", "type": "string", "description": "End date for filed cases (YYYY-MM-DD)" }, "openai_api_key": { "title": "OpenAI API Key", "type": "string", "description": "OpenAI API key for enhanced text extraction (optional - will use regex fallback if not provided)" }, "gcs_bucket_name": { "title": "GCS Bucket Name", "type": "string", "description": "Google Cloud Storage bucket name (optional - defaults to 'courts-bucket')" } } }, "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 } } } } } } } } } } ```