{"id":1667,"date":"2026-08-20T09:00:00","date_gmt":"2026-08-20T00:00:00","guid":{"rendered":"https:\/\/blog.hancom.com\/unstructured-data-parsing-ai-document-parsing\/"},"modified":"2026-08-25T11:44:52","modified_gmt":"2026-08-25T02:44:52","slug":"unstructured-data-parsing-ai-document-parsing","status":"publish","type":"post","link":"https:\/\/blog.hancom.com\/en\/unstructured-data-parsing-ai-document-parsing\/","title":{"rendered":"Why Unstructured Data Parsing Is Difficult and How to Structure It with AI Document Parsing"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">You feed documents into your RAG system, only to find that tables break apart and answers come out wrong, or text is extracted but the connection to its corresponding field is lost. If you&#8217;ve worked on digitizing documents from public institutions or financial organizations, this scenario is all too familiar. The root cause lies in the nature of the data itself. Unstructured data\u2014which makes up the majority of enterprise information\u2014lacks a predefined structure, making it difficult to analyze in its raw form. In this article, we examine why unstructured data parsing is challenging and how AI document parsing enables structuring.    <\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>What Is Unstructured Data?<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">Unstructured data refers to information without a predefined structure such as rows and columns (schema). Examples include documents, emails, images, and videos\u2014formats that vary in shape and are difficult to analyze directly using traditional data tools.<\/p>\n\n<h3 class=\"wp-block-heading\"><strong>What Does Unstructured Data Mean, and How Does It Differ from Structured Data?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Unstructured data is information without a predefined data model or schema, in contrast to structured data, which is organized into rows and columns. The key difference lies in when structure is applied. <\/p>\n\n<p class=\"wp-block-paragraph\">According to <a href=\"https:\/\/www.ibm.com\/think\/topics\/structured-vs-unstructured-data\" target=\"_blank\" rel=\"noopener\">IBM technical documentation<\/a> comparing structured and unstructured data, structured data has a fixed schema, is stored in relational databases (RDBMS) as rows and columns, and is queried using SQL. Unstructured data, on the other hand, has no fixed schema and exists in complex formats such as audio or web pages, making it difficult to fit into traditional databases. <\/p>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Criteria<\/strong><\/td><td><strong>Structured Data<\/strong><\/td><td><strong>Unstructured Data<\/strong><\/td><\/tr><tr><td>Presence of Structure<\/td><td>Fixed schema (rows and columns)<\/td><td>No predefined structure<\/td><\/tr><tr><td>Storage Location<\/td><td>Relational database (RDBMS)<\/td><td>Data lake, etc. (raw form)<\/td><\/tr><tr><td>Query Method<\/td><td>Queried via SQL<\/td><td>Requires separate processing and analysis<\/td><\/tr><tr><td>Schema Application Timing<\/td><td>Schema-on-write (structured before storage)<\/td><td>Schema-on-read (applied at time of use)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<h3 class=\"wp-block-heading\"><strong>What Types of Unstructured Data Exist?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Unstructured data exists in a wide variety of forms, from text-based data such as documents and emails to images, audio, and video.<\/p>\n\n<ul class=\"wp-block-list\">\n<li>Non-text-based: Images (JPEG, GIF, PNG), multimedia, video, mobile activity and IoT sensor data<\/li>\n\n\n\n<li>Text-based: Email, Word documents, PDFs, blog and social media posts, call transcripts and message text<\/li>\n<\/ul>\n\n<p class=\"wp-block-paragraph\">Among these, the most challenging to handle in enterprise settings are text-based documents such as reports, contracts, and official correspondence that mix tables and hierarchical structures\u2014representing the highest level of parsing difficulty.<\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview-1024x576.png\" alt=\"An image explaining the complexity of unstructured data parsing through four factors: format diversity, structural complexity, data consistency, and closed-format limitations\" class=\"wp-image-1262\" srcset=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview-1024x576.png 1024w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview-300x169.png 300w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview-768x432.png 768w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview-600x338.png 600w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-complexity-overview.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>Why Unstructured Data Parsing Is Difficult<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">Because unstructured data lacks a predefined structure, simply reading text in sequence damages table and structural information. This corrupted data then flows through the retrieval and generation stages of RAG, degrading overall quality.<\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Parsing Challenges Arising from the Absence of Structure<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Unstructured data is difficult to analyze directly with traditional data processing tools because it lacks a predefined data model. Structure must be newly applied at the point of use, and restoring that structure is itself a complex task. <\/p>\n\n<p class=\"wp-block-paragraph\">A representative challenge is tables within documents. The <a href=\"https:\/\/arxiv.org\/abs\/2203.09056\" target=\"_blank\" rel=\"noopener\">RobusTabNet study<\/a> on table recognition identifies the stable detection of table location and structure across varying formats as a core challenge. The <a href=\"https:\/\/arxiv.org\/abs\/2404.13268\" target=\"_blank\" rel=\"noopener\">Multi-Cell Decoder study<\/a>, which addresses both table structure and character recognition, also views the automatic recognition of table structure and text as a problem that remains difficult to solve. Even tables that appear simple to humans require dedicated research for machines to accurately interpret their structure.   <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Cases Where Simple OCR and Text Extraction Damage Unstructured Data<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Simple text extraction methods ignore document structure and read characters in sequence, causing information to become scrambled in tables or multi-column layouts.<\/p>\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/arxiv.org\/abs\/2412.02592\" target=\"_blank\" rel=\"noopener\">OHRBench study<\/a>, which evaluates the impact of OCR (Optical Character Recognition) on RAG, defines two types of OCR noise: semantic noise, where characters are misread, and formatting noise, where structural representations such as tables and layouts are distorted. The core finding of the study is that even when characters are read correctly, remaining formatting errors degrade RAG quality.  <\/p>\n\n<p class=\"wp-block-paragraph\">Even the best-performing pipeline in the evaluation, <a href=\"https:\/\/arxiv.org\/abs\/2412.02592\" target=\"_blank\" rel=\"noopener\">OCR (MinerU)<\/a>, showed a 2.93-point drop in answer accuracy when using OCR-extracted text compared to answering from error-free source documents. Errors introduced during OCR conversion directly transferred to answer quality. When isolating formatting noise alone, performance dropped by up to approximately 16.8% in retrieval and up to approximately 19.4% in generation for certain queries and models.  <\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow-1024x576.png\" alt=\"An image showing the AI document processing flow from document input through document analysis, data extraction, to data utilization during unstructured data parsing\" class=\"wp-image-1263\" srcset=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow-1024x576.png 1024w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow-300x169.png 300w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow-768x432.png 768w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow-600x338.png 600w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-process-flow.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h3 class=\"wp-block-heading\"><strong>Why Extracting Text or Table Data from HWP Files Is Difficult<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">HWP files are stored in a binary format that cannot be read directly by humans, and extracting content is difficult without dedicated software. As a result, HWP documents accumulated in public institutions and financial organizations face challenges from the very start of digitization. <\/p>\n\n<p class=\"wp-block-paragraph\">The degree of difficulty becomes clear when examining the internal structure of the format. According to <a href=\"https:\/\/tech.hancom.com\/%ED%95%9C-%EA%B8%80-%EB%AC%B8%EC%84%9C-%ED%8C%8C%EC%9D%BC-%ED%98%95%EC%8B%9D-hwp-%ED%8F%AC%EB%A7%B7-%EA%B5%AC%EC%A1%B0-%EC%82%B4%ED%8E%B4%EB%B3%B4%EA%B8%B0\/\" target=\"_blank\" rel=\"noopener\">Hancom Tech&#8217;s technical article<\/a> explaining the HWP format structure, HWP documents are divided into multiple streams such as DocInfo and BodyText, with each data unit stored as a record containing type, size, and content, followed by compression. This is why simple character extraction methods cannot restore such a multi-layered structure.  <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\ud83d\udc49 <\/strong><a href=\"https:\/\/tech.hancom.com\/%ED%95%9C-%EA%B8%80-%EB%AC%B8%EC%84%9C-%ED%8C%8C%EC%9D%BC-%ED%98%95%EC%8B%9D-hwp-%ED%8F%AC%EB%A7%B7-%EA%B5%AC%EC%A1%B0-%EC%82%B4%ED%8E%B4%EB%B3%B4%EA%B8%B0\/\" target=\"_blank\" rel=\"noopener\"><strong>Read more on the Hancom Tech blog<\/strong><\/a><\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Why Are Tables (Merged Cells and Multi-Level Headers) Not Extracted Accurately?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">In tables, the structure itself carries meaning through the relationships between cells. Restoring that structure accurately is not straightforward, especially for merged cells or tables with unclear boundaries.<\/p>\n\n<p class=\"wp-block-paragraph\">The core difficulty lies in &#8216;invisible structure.&#8217; The <a href=\"https:\/\/arxiv.org\/abs\/2404.13268\" target=\"_blank\" rel=\"noopener\">Multi-Cell Decoder study<\/a> views the joint recognition of table structure and text as a key challenge in knowledge information processing, treating vertically and horizontally merged cells and tables with invisible borders as difficult recognition targets. The <a href=\"https:\/\/arxiv.org\/abs\/2203.09056\" target=\"_blank\" rel=\"noopener\">RobusTabNet study<\/a> also identifies tables with large empty spaces or distorted shapes as core challenges, proposing a method that divides tables into cell grids and then reconstructs merged cells. <\/p>\n\n<p class=\"wp-block-paragraph\">The recent <a href=\"https:\/\/arxiv.org\/abs\/2506.07015\" target=\"_blank\" rel=\"noopener\">TABLET study<\/a> on table recognition also proposed a model that restores complex table structures by splitting rows and columns and then re-merging cells. Accurately reading tables with merged cells and multi-level headers remains a challenge that is still being addressed. <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Why Unstructured Data Processing Quality Affects the Entire RAG Pipeline<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">When document structure is damaged and only text is extracted, incorrect information is retrieved at the search stage, ultimately leading to hallucinations where the LLM generates inaccurate answers.<\/p>\n\n<p class=\"wp-block-paragraph\">Typically, the RAG process follows the flow: document extraction \u2192 parsing \u2192 chunking \u2192 embedding \u2192 vector DB storage \u2192 retrieval \u2192 LLM answer generation. <\/p>\n\n<p class=\"wp-block-paragraph\">Inaccurate extraction cascades through subsequent stages. The <a href=\"https:\/\/arxiv.org\/abs\/2509.11552\" target=\"_blank\" rel=\"noopener\">HiChunk study<\/a> on hierarchical chunking also points out the lack of evaluation tools for existing chunking methods and proposes that chunking that preserves document hierarchical structure is key to improving retrieval quality. The solution to this problem is AI document parsing that preserves structure.  <\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison-1024x576.png\" alt=\"An image comparing the limitations of general OCR in unstructured data parsing with the differentiators of Hancom Data Loader, including HWP SDK source technology and direct PDF analysis\" class=\"wp-image-1264\" srcset=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison-1024x576.png 1024w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison-300x169.png 300w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison-768x432.png 768w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison-600x338.png 600w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ocr-vs-hancom-comparison.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>How to Structure Unstructured Data with AI Document Parsing<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">AI document parsing analyzes both the structure and role of document elements, converting them into structured data formats such as JSON while preserving tables, hierarchies, and metadata.<\/p>\n\n<h3 class=\"wp-block-heading\"><strong>How Does AI Document Parsing Differ from General Text Extraction?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">OCR reads characters from images and converts them into text only, whereas document parsing also analyzes the structure and role of that text.<\/p>\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/arxiv.org\/abs\/2410.21169\" target=\"_blank\" rel=\"noopener\">document parsing survey<\/a> on structured information extraction treats DLA (Document Layout Analysis) as a fundamental preprocessing step for document understanding, distinguishing OCR and structural analysis as separate domains. <a href=\"https:\/\/arxiv.org\/abs\/2510.13366\" target=\"_blank\" rel=\"noopener\">Another study<\/a> also explains that VLM (Vision-Language Model)-based approaches combine image features with text for end-to-end processing, overcoming the limitations of traditional OCR methods.<\/p>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Criteria<\/strong><\/td><td><strong>OCR<\/strong><\/td><td><strong>Document Parsing<\/strong><\/td><\/tr><tr><td>Processing Target<\/td><td>Characters in images<\/td><td>Entire document (text, tables, images)<\/td><\/tr><tr><td>Output<\/td><td>Text<\/td><td>Data with preserved structure<\/td><\/tr><tr><td>Structure Recognition<\/td><td>None<\/td><td>Distinguishes and restores titles, body text, and tables<\/td><\/tr><tr><td>Application<\/td><td>Character digitization<\/td><td>Structured data for RAG and AI training<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p class=\"wp-block-paragraph\">Hancom Data Loader is a solution that performs exactly this document parsing, analyzing documents based on DLA, OCR, and TSR (Table Structure Recognition).<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\ud83d\udc49 <\/strong><a href=\"https:\/\/livedemo.sdk.hancom.com\/dataloader\" target=\"_blank\" rel=\"noopener\">Explore the Hancom Data Loader live demo<\/a><\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Parsing Process by Document Type (HWP, PDF, PDF AI, OOXML)<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Hancom Data Loader follows a defined process for each of the four supported input formats\u2014HWP, PDF, PDF AI, and OOXML\u2014to deliver structured final output.<\/p>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Input Format<\/strong><\/td><td><strong>Processing Flow<\/strong><\/td><\/tr><tr><td>HWP<\/td><td>Document \u2192 Data attribute analysis \u2192 Data and information extraction \u2192 AI enhancement \u2192 Data and information refinement \u2192 Final output delivery<\/td><\/tr><tr><td>PDF AI<\/td><td>Document \u2192 DLA (AI-based) \u2192 Data and information extraction \u2192 Final output delivery<\/td><\/tr><tr><td>PDF<\/td><td>Document \u2192 Rule-based analysis \u2192 Data and information extraction \u2192 Final output delivery<\/td><\/tr><tr><td>OOXML<\/td><td>Document \u2192 Data and information extraction \u2192 Final output delivery<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p class=\"wp-block-paragraph\">This processing operates through two engines in parallel: rule-based analysis for quickly handling structured documents, and AI-based document understanding for precisely handling unstructured documents with mixed images and tables. While OOXML focuses primarily on text extraction, HWP, HWPX, and PDF AI include structural analysis stages. <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>How to Extract Text and Tables from HWP Files Using AI Document Parsing<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">HWP and HWPX files are processed by parsing the original file directly, preserving table structure, footnotes, and metadata without requiring conversion to PDF. This approach directly addresses the problem identified earlier: &#8216;HWP is a binary format that is difficult to read with general tools.&#8217; <\/p>\n\n<p class=\"wp-block-paragraph\">Converting to PDF before extraction often causes structural information such as tables and footnotes to be lost in the process. Direct parsing of the original minimizes information loss and preserves it intact. HWP and HWPX documents also provide hierarchical structure information based on paragraphs, extracting how titles, body text, and tables are organized in relation to one another. Given that a significant portion of public and financial documents are stored in this format, which is optimized for the domestic work environment, the ability to handle originals directly plays a key role in transforming previously inaccessible materials into usable data.  <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Can Tables with Merged Cells and Multi-Level Headers Be Extracted Accurately?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">TSR technology identifies relationships between cells to restore the logical structure of borderless tables, merged cells, and even nested tables.<\/p>\n\n<p class=\"wp-block-paragraph\">TSR is a technology that converts tables in images into logical coordinates or markup sequences. Because it first identifies how cells are connected before reading characters in sequence, it maintains the connection between headers and values even in tables with merged cells or multi-row headers. The restored tables are output in Markdown, JSON, or HTML at the cell, row, and column level, ready for immediate use without additional post-processing. This is especially important for public and financial documents where the table itself carries meaning.  <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>In What Format Is Document Parsing Output Delivered?<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Document parsing output is delivered in structured data formats such as JSON, HTML, and Clientinfo, depending on the usage environment and input format.<\/p>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Usage Environment<\/strong><\/td><td><strong>Input Format<\/strong><\/td><td><strong>Output Format<\/strong><\/td><\/tr><tr><td>Studio<\/td><td>PDF \/ PDF AI<\/td><td>JSON, HTML, Viewer<\/td><\/tr><tr><td>Studio<\/td><td>HWP, HWPX<\/td><td>JSON, Viewer<\/td><\/tr><tr><td>Solution<\/td><td>PDF AI, HWP, HWPX<\/td><td>JSON, Clientinfo<\/td><\/tr><tr><td>Solution<\/td><td>OOXML<\/td><td>Clientinfo<\/td><\/tr><tr><td>API SaaS<\/td><td>PDF, HWP, HWPX<\/td><td>JSON<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p class=\"wp-block-paragraph\">The key point is that unstructured data is output in structured formats such as JSON. Structure-preserved JSON can be used directly for RAG integration, AI training data acquisition, and search indexing, significantly reducing the work required in subsequent stages. <\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>Types of Document Parsing Solutions and Selection Criteria<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">When choosing a document parsing solution, compare based on table and structure preservation capability, supported input format range, and support for secure environments such as closed networks.<\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Criteria for Selecting Unstructured Data Preprocessing Tools for RAG<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Table and structure preservation capability, supported input format range, and secure environment support\u2014these three are the core criteria for selecting a RAG document preprocessing tool.<\/p>\n\n<p class=\"wp-block-paragraph\">Because structural damage (formatting noise) causes cascading performance degradation in both the retrieval and generation stages of RAG, structure preservation capability is the first criterion. Additionally, the <a href=\"https:\/\/arxiv.org\/abs\/2011.13534\" target=\"_blank\" rel=\"noopener\">deep learning survey<\/a> for OCR and document understanding explains that end-to-end systems integrating document layout analysis, text recognition, and information extraction are becoming increasingly important in the field of document understanding. In summary, it is advisable to confirm the following three points:  <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\u2705 Table and Structure Preservation Capability<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">Does it extract tables with merged cells and multi-level headers while preserving relationships?<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\u2705 Supported Input Format Range<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">Does it handle the document formats you actually possess, such as HWP, HWPX, PDF, and OOXML?<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\u2705 Secure Environment Support<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">Can it be operated in on-premises or closed-network environments?<\/p>\n\n<p class=\"wp-block-paragraph\">\ud83d\udca1 Looking for a solution that meets all three criteria\u2014from table and structure preservation to secure environment support?<\/p>\n\n<p class=\"wp-block-paragraph\">\ud83d\udc49 <a href=\"https:\/\/sdk.hancom.com\/services\/1?type=DATA_LOADER\" target=\"_blank\" rel=\"noopener\">Learn more about Hancom Data Loader<\/a><\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example-1024x576.png\" alt=\"An example image showing how AI recognizes table structure and merged cells in the original document during unstructured data parsing and converts them into structured data\" class=\"wp-image-1265\" srcset=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example-1024x576.png 1024w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example-300x169.png 300w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example-768x432.png 768w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example-600x338.png 600w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-ai-public-document-example.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h3 class=\"wp-block-heading\"><strong>Comparison of Document AI Solutions by Closed-Network Environment Support<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Document AI solutions are divided into cloud-based methods that transmit documents to external servers and on-premises methods that install on internal servers so documents do not leave the organization.<\/p>\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Criteria<\/strong><\/td><td><strong>On-Premise<\/strong><\/td><td><strong>SaaS API (Cloud)<\/strong><\/td><\/tr><tr><td>Installation Location<\/td><td>Customer&#8217;s internal server<\/td><td>External cloud integration<\/td><\/tr><tr><td>External Data Transmission<\/td><td>None<\/td><td>Transmitted to external server<\/td><\/tr><tr><td>Billing<\/td><td>Build-out model<\/td><td>Per-page billing<\/td><\/tr><tr><td>Suitable Environment<\/td><td>Public institutions, finance, and other closed networks<\/td><td>Rapid adoption and pilot projects<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<p class=\"wp-block-paragraph\">Hancom Data Loader offers both methods. On-premises installs and operates directly on the customer&#8217;s internal server, ensuring documents do not leave the organization, making it suitable for high-security closed networks such as public institutions and finance. SaaS API integrates immediately without a separate server and bills per page, making it ideal for starting quickly without initial investment or for pilot use before on-premises deployment.  <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>Benefits of Automating Document Parsing<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">Automating document parsing enables immediate use of structured data with preserved tables, hierarchies, and metadata for RAG and AI training, significantly reducing post-extraction work. However, for documents where automatic extraction alone cannot ensure accuracy, Data Loader Studio (an extended solution) allows users to review results directly and make necessary corrections. <\/p>\n\n<p class=\"wp-block-paragraph\">Data Loader Studio assists in review by displaying preprocessed extraction results side by side with the original document. Users can directly verify and correct hierarchy, category, and reading order to refine document structure and semantic relationships to fit business purposes. Semantic-based tagging and labeling can supplement extraction criteria tailored to customer formats, refining data to a quality suitable for training and search.  <\/p>\n\n<p class=\"wp-block-paragraph\">\ud83d\udca1 Want to see firsthand how your HWP and PDF documents are structured?<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\ud83d\udc49 <\/strong><a href=\"https:\/\/sdk.hancom.com\/contacts\/create\" target=\"_blank\" rel=\"noopener\">Inquire about Hancom Data Loader adoption<\/a><\/p>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>Frequently Asked Questions (FAQ) About Unstructured Data Parsing<\/strong><\/h2>\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1784003894719\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong><strong><strong><strong><strong><strong>What Is the Biggest Difference Between Unstructured Data and Structured Data?<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The presence or absence of a schema (predefined structure) is the key difference. Structured data is stored with a row-and-column schema, while unstructured data is stored in its original form without a schema. <\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1784003900806\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong><strong><strong><strong><strong><strong>Can HWP Files Be Parsed with AI Document Parsing?<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Hancom Data Loader parses HWP and HWPX originals directly, extracting tables, footnotes, and metadata without requiring PDF conversion. <\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1784003909493\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong><strong><strong><strong><strong><strong>Are Documents with Nested Tables or Merged Cells Extracted Accurately?<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>TSR technology handles this. It identifies relationships between cells to restore the logical structure of nested tables, merged cells, and borderless tables. <\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785825073813\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Can Document Parsing Solutions Be Used in Closed-Network Environments?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, through on-premises deployment. Installation on internal servers ensures documents are not transmitted externally, making it suitable for public and financial closed networks. <\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1785825075883\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>Can Document Parsing Results Be Used Directly in RAG Systems?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Output in structured formats such as JSON enables direct integration into RAG pipelines. Preserved structure reduces the post-processing burden in retrieval and generation stages. <\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features-1024x576.png\" alt=\"\" class=\"wp-image-1266\" srcset=\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features-1024x576.png 1024w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features-300x169.png 300w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features-768x432.png 768w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features-600x338.png 600w, https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/08\/unstructured-data-parsing-hancom-data-loader-features.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><strong>Unstructured Data Parsing: Ensuring RAG Quality with AI Document Parsing<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<p class=\"wp-block-paragraph\">The challenges of unstructured data parsing can be summarized in three main points: parsing is difficult due to the absence of a predefined structure; corrupted extraction results flow through the retrieval and generation stages of RAG, degrading quality; and therefore, AI document parsing that preserves structure becomes the key to quality. <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>\ud83d\udda5\ufe0f<\/strong><a href=\"https:\/\/sdk.hancom.com\/services\/1?type=DATA_LOADER\" target=\"_blank\" rel=\"noopener\"><strong>Hancom Data Loader<\/strong><\/a><\/h3>\n\n<p class=\"wp-block-paragraph\">Hancom Data Loader is a document parsing solution that parses HWP and HWPX originals directly, restores complex tables with TSR, and handles closed-network documents in on-premises environments. You can see firsthand in the live demo how documents that were previously difficult to digitize are transformed into structured data ready for immediate use in RAG. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>\ud83d\udc49 <\/strong><a href=\"https:\/\/livedemo.sdk.hancom.com\/dataloader\" target=\"_blank\" rel=\"noopener\">Experience the Hancom Data Loader live demo<\/a><\/p>\n\n<p class=\"wp-block-paragraph\">\ud83d\udc49 <a href=\"https:\/\/sdk.hancom.com\/contacts\/create\" target=\"_blank\" rel=\"noopener\">Inquire About Hancom Data Loader<\/a><\/p>\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n<h2 class=\"wp-block-heading\"><strong>References<\/strong><\/h2>\n\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<ol class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.ibm.com\/think\/topics\/structured-vs-unstructured-data\" target=\"_blank\" rel=\"noopener\">IBM<\/a>, &#8220;Structured vs. Unstructured Data: What&#8217;s the Difference?&#8221;<\/li>\n\n\n\n<li><a href=\"https:\/\/www.ibm.com\/think\/topics\/unstructured-data\" target=\"_blank\" rel=\"noopener\">IBM<\/a>, &#8220;What Is Unstructured Data?&#8221;<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2203.09056\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;Robust Table Detection and Structure Recognition from Heterogeneous Document Images&#8221; (RobusTabNet), 2022<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2404.13268\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition,&#8221; 2024<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2506.07015\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;TABLET: Table Structure Recognition using Encoder-only Transformers,&#8221; 2025<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2412.02592\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation&#8221; (OHRBench), 2025<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2509.11552\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking&#8221; (HiChunk), 2025<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2410.21169\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, \u201cDocument Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction,\u201d 2024<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2510.13366\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;Document Intelligence in the Era of Large Language Models: A Survey,&#8221; 2025<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2011.13534\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;A Survey of Deep Learning Approaches for OCR and Document Understanding,&#8221; 2020<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/abs\/2412.02592\" target=\"_blank\" rel=\"noopener\">arXiv<\/a>, &#8220;OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation&#8221; (OHRBench), 2024 <\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>This article identifies the challenges of unstructured data parsing\u2014spanning table handling, HWP format constraints, and OCR limitations\u2014and explains how AI document parsing transforms HWP, HWPX, PDF, and OOXML files into structured JSON data, drawing on research papers and official technical documentation.<\/p>\n","protected":false},"author":2,"featured_media":1668,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26,10],"tags":[],"class_list":["post-1667","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ax-insight","category-10"],"_links":{"self":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/1667","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/comments?post=1667"}],"version-history":[{"count":6,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/1667\/revisions"}],"predecessor-version":[{"id":1772,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/1667\/revisions\/1772"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/media\/1668"}],"wp:attachment":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/media?parent=1667"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/categories?post=1667"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/tags?post=1667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}