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    DocumentAI

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    Sold by: INTERX 
    Deployed on AWS
    DocumentAI is an AI-powered solution that converts unstructured documents including PDF, HWP, DOCX, PPTX into structured JSON data. It accurately parses text, tables, formulas, and code, enabling seamless integration into AI training, search systems, and automation workflows.

    Overview

    Document Parse AI Solution for Unstructured Document Parsing DocumentAI is an AI powered document parsing solution that transforms various unstructured documents into structured data. It supports a wide range of formats including PDF, HWP, PPTX, DOCX, and more. It precisely analyzes not only text but also tables, formulas, code, images, and other elements within documents. The parsed output is provided in a standard JSON format, arranged according to the natural reading flow, making it easy for developers to use in AI training, search systems, automation tasks, and more. Reduce preprocessing time and maximize the value of unstructured data with DocumentAI.

    Highlights

    • Key Strengths Accurate Layout Analysis: Automatically detects document structure and provides high-quality structured data. Ensures consistent parsing across diverse formats and layouts. Wide Format Support: Supports PDF, DOCX, HWP, PPTX, XLSX, and image files. Enables unified processing of documents from various sources. JSON-Based Output: Delivers human-readable JSON results. Easily integrated into AI training, search, and automation systems.
    • Key Features Text OCR: Extracts text from images or PDFs, including metadata-based parsing. Layout Analysis: Classifies content into 10 types (text, tables, formulas, code, etc.). Table Parsing: Converts tables, including image-based ones, into structured HTML. Formula Parsing: Extracts formulas from tables or standalone and converts to LaTeX. Text Parsing: Segments and organizes text by semantic units. JSON Output: Provides all results in a unified, readable JSON format.
    • Key Applications LLM Preprocessing: Structures large volumes of documents to build quality training datasets. RAG System Development: Parses various formats to construct knowledge bases for RAG systems. Enterprise KMS: Converts unstructured documents (e.g., reports, manuals) into searchable, reusable data. Automated Report Generation: Generates formatted reports automatically using parsed data.

    Details

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    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu 22.04

    Deployed on AWS

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    Pricing

    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (1)

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    Dimension
    Cost/hour
    g6.xlarge
    Recommended
    $15.00

    Vendor refund policy

    We do not support any refunds currently.

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    Usage information

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    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes

    Added enhanced security features to improve overall product protection.

    Additional details

    Usage instructions

    Input Supported File Formats: PDF, DOCX, PPTX, HWPX, HWP

    Upload Methods: Files can be uploaded individually through the web interface or automatically via API.

    Input data

    POST /api/v1/document/parse
    Content-Type: multipart/form-data
    Authorization: Bearer YOUR_API_KEY Request Body: { "file": binary, "title": "File Name", "subtitle": "Sub Title" }

    Output Format (Structured JSON) The output is provided in a structured JSON format, sorted in a natural reading order.

    Top-Level Fields msg_id: Unique ID for server communication

    subtitle: Subtitle assigned by the user to the document

    title: Actual title of the document

    save_time: Timestamp of the document request

    result Object api Version of the API used for processing

    content html: Entire content of the analyzed document in HTML format

    markdown: Entire content in Markdown format

    text: Entire content in plain text format

    elements (Array of document components) Each element includes:

    category: Recognized class name (e.g., plain_text, figure, caption, etc.)

    content:

    text: The raw textual content of the element

    markdown: Markdown representation

    html: HTML representation

    coordinates:

    Four (x, y) coordinates indicating the position of the element

    Represent the top-left, top-right, bottom-right, and bottom-left corners

    conf: Confidence score of the recognition (float between 0 and 1)

    id: Unique ID assigned to the element (integer)

    page: Page number where the element appears

    order: Reading order of the element on the page (integer)

    relation (optional): List of related element IDs (e.g., caption linked to a figure or table)

    model Version of the AI model used (e.g., "interx_doc_ai_250509")

    pages Total number of pages in the document

    Latency_per_module table: Time taken for table recognition (in seconds)

    formula: Time taken for formula recognition (in seconds)

    text: Time taken for text analysis (in seconds)

    end2end_execution_time: Total end-to-end processing time (in seconds)

    Output data { "msg_id": string, "subtitle": string, "title": string, "save_time": string, "result": { "api": string, "content": { "html": string, "markdown": string, "text": string }, "elements": [ { "category": string, "content": { "text": string, "markdown": string, "html": string }, "coordinates": [ { "x": float, "y": float }, { "x": float, "y": float }, { "x": float, "y": float }, { "x": float, "y": float } ], "conf": float, "id": integer, "page": integer, "order": integer, "relation": [integer] (optional) } ], "model": string, "pages": integer, "Latency_per_module": { "table": float, "formula": float, "text": float, "end2end_execution_time": float } } }

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