{"id":496,"date":"2026-05-15T10:00:00","date_gmt":"2026-05-15T01:00:00","guid":{"rendered":"https:\/\/blog.hancom.com\/what-is-ocr-from-ai-ocr-core-technologies-to-a-document-automation-guide\/"},"modified":"2026-07-16T15:18:24","modified_gmt":"2026-07-16T06:18:24","slug":"what-is-ocr-ai-ocr-document-automation-guide","status":"publish","type":"post","link":"https:\/\/blog.hancom.com\/en\/what-is-ocr-ai-ocr-document-automation-guide\/","title":{"rendered":"What is OCR? From AI OCR Core Technologies to a Document Automation Guide"},"content":{"rendered":"\n

It’s common to encounter situations where table structures break down after implementing OCR, HWP\u00b7HWPX files cannot be processed at all, or document preprocessing gets stuck. We will systematically explain everything from the meaning and principles of OCR to AI OCR core technologies and the document automation pipeline. <\/p>\n\n

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What is OCR? Definition and Concepts <\/strong><\/h2>\n\n

OCR (Optical Character Recognition)<\/strong> is a technology that converts text images into machine-readable format.<\/p>\n\n

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OCR Definition and Key Features<\/strong><\/h3>\n\n

OCR extracts data from scanned documents, camera images, and image-only PDFs, transforming it into editable and searchable text. OCR software identifies characters in images, forms them into words, and then converts words into sentences, allowing access to and editing of the original content. Scanning official administrative documents, automatic contract entry, and extracting text from image-based PDFs are all based on this technology. <\/p>\n\n

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How Does OCR Work? Principles of Operation<\/strong><\/h3>\n\n

OCR operates through three stages: image preprocessing \u2192 character recognition \u2192 post-processing.<\/strong><\/strong><\/p>\n\n

In image preprocessing, noise is removed, and skew is corrected. In character recognition, the system analyzes areas within the image, distinguishing characters from the background using pattern recognition algorithms. In post-processing, misrecognized characters are corrected, and the output is editable and searchable text data. <\/p>\n\n

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History of OCR Technology and AI OCR Core Technologies<\/strong><\/h2>\n\n

History and Types of Traditional OCR Methods<\/strong><\/h3>\n\n

OCR technology was first developed in the early 20th century to help visually impaired individuals read printed text.<\/p>\n\n

By the 1950s, banks and the postal industry began commercial adoption<\/strong> to automatically extract data from standardized forms. However, early OCR could only read predefined fonts and layouts, leading to errors with even slight variations in format. It worked well for structured and predictable documents but failed to perform adequately for others. <\/p>\n\n

By the 1990s, it evolved to recognize various fonts and dozens of languages<\/strong>. While its application scope significantly expanded, processing unstructured data, handwriting, and documents with unusual layouts remained challenging. Manual review was essential for most document processing tasks. <\/p>\n\n

A true turning point arrived in the 2000s<\/strong>. The integration of AI and machine learning into OCR systems enabled the processing of a much wider variety of documents with higher accuracy. The systems learned from processed documents, adapted to new formats, and dramatically reduced manual input and error rates. <\/p>\n\n

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Three Core AI OCR Technologies<\/strong><\/h3>\n\n

The core technologies that constitute Document Understanding are OCR, DLA, and TSR<\/strong>. With the integration of VLM (Vision-Language Model) technology, this is expanding to include the understanding of unstructured visual information such as images and charts. <\/p>\n\n

Document Layout Analysis (DLA) automatically distinguishes titles, tables, and image areas within a document. Just as humans instantly grasp a document’s structure, DLA enables AI to first understand the overall layout of a document. <\/p>\n\n

Table Structure Recognition (TSR) restores row-column relationships even in tables without borders and merged cells, converting them into structured data. While traditional OCR treated tables as mere blocks of text, TSR preserves the cell-level structure, outputting it in a format directly usable by LLM and RAG. <\/p>\n\n

VLM-based Image Captioning automatically generates searchable text descriptions for charts and diagrams. Since information within images is converted into text, visual information can also be utilized during AI searches. \u203b The Image Captioning feature is currently in the PoC stage, and the commercial release schedule will be announced later.<\/em><\/p>\n\n

Hancom Data Loader includes Semantix, which integrates Document Layout Analysis (DLA), Table Structure Recognition (TSR), VLM-based Image Captioning, and OCR into a single pipeline. <\/p>\n\n

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\"Image<\/figure>\n\n
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OCR Application Methods and Industry-Specific Use Cases<\/strong><\/h2>\n\n

OCR Use Cases in Finance, Public Sector, Healthcare, and Logistics?<\/strong><\/h3>\n\n

AI OCR automates manual data entry across various industries, including finance, healthcare, and logistics.<\/strong><\/p>\n\n

In finance and banking, it automatically processes and verifies documents required for financial transactions, such as invoices, receipts, loan documents, and check deposits. In healthcare, it processes patient records like treatment, examination, and hospitalization records, streamlining workflows and reducing manual tasks in hospitals. In logistics, it extracts information from package labels, invoices, and bills of lading (BOLs) to efficiently track documents. Additionally, businesses and institutions can convert existing documents into searchable digital archives, allowing for quick retrieval of necessary information from large volumes of files. <\/p>\n\n

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Document Automation with OCR in the AI Era: Why Text Recognition Alone Is Insufficient<\/strong><\/h2>\n\n
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Difference Between OCR and Parser<\/strong><\/h3>\n\n

In the AI era, OCR requires structured document output, not just simple text extraction.<\/p>\n\n

With the introduction of LLM and RAG into practical applications, the demands on OCR have changed. Extracting only text from scanned PDFs makes it difficult to properly operate an AI pipeline. If merged cells are broken, multi-column layout order is scrambled, or the hierarchy of titles, subtitles, and body text is lost, AI cannot grasp the context. <\/p>\n\n

If OCR reads ‘what is written,’ Document Parsing understands ‘how it is structured.’ Both stages must work together for complete document AI integration. <\/p>\n\n

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Overview of the Differences Between OCR and Document Parser<\/strong><\/h4>\n\n
Category<\/strong><\/td>OCR<\/strong><\/td>Document Parser<\/strong><\/td><\/tr>
Role<\/td>Converts characters in images to text<\/td>Analyzes document structure, hierarchy, and meaning<\/td><\/tr>
Output<\/td>Flat text<\/td>Structured data (JSON, HTML)<\/td><\/tr>
Limitations<\/td>Cannot understand table, hierarchy, or image structures<\/td>Cannot process image documents independently<\/td><\/tr>
Application<\/td>Simple text digitization<\/td>RAG, AI system pipeline integration<\/td><\/tr><\/tbody><\/table><\/figure>\n\n

\ud83d\udca1 If you are considering both HWP\u00b7HWPX file processing and AI system integration, check out the live demo of Hancom Data Loader first. <\/p>\n\n

\ud83d\udc49<\/a>Explore Hancom Data Loader<\/a><\/strong><\/p>\n\n

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\"An<\/figure>\n\n
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Can General Solutions Process HWP Korean Documents with OCR?<\/strong><\/h2>\n\n

General OCR solutions cannot directly process HWP\u00b7HWPX files. This is because they do not support HWP\u00b7HWPX, requiring prior PDF conversion, which often leads to document structure loss. <\/p>\n\n

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Limitations of General OCR Solutions for HWP\u00b7HWPX Processing and the Need for Direct Original Parsing<\/strong><\/h3>\n\n

A significant portion of domestic public, financial, and legal documents are created in HWP\u00b7HWPX format. However, most OCR solutions, such as AWS Textract, Google Document AI, and Tesseract, do not directly support HWP\u00b7HWPX, necessitating a conversion process.<\/strong> <\/p>\n\n

During this process, over 20 types of layout elements, including change tracking, footnotes, formulas, and bullet points, are lost. Tables with merged cells are converted into images, making their content unrecognizable. <\/p>\n\n

Hancom Data Loader directly analyzes all HWP versions 3.0 and above from the original source using HWP SDK core technology, without PDF conversion, outputting structured data in JSON or HTML while preserving over 20 types of layout information<\/strong>.<\/p>\n\n

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A Quick Look at the<\/strong>Difference Between <\/strong>PDF Conversion<\/strong> and <\/strong>Direct Original Parsing<\/strong><\/h4>\n\n
Item<\/strong><\/td>PDF Conversion Method<\/strong><\/td>Direct Original Parsing<\/strong><\/td><\/tr>
Processing Method<\/td>HWP\u00b7HWPX \u2192 PDF conversion, then OCR<\/td>Direct analysis of original HWP\u00b7HWPX files<\/td><\/tr>
Table Data<\/td>Converted to image, unrecognizable<\/td>Structure preserved, cell-unit extraction<\/td><\/tr>
Formatting Information<\/td>Footnotes, indents, bullets lost<\/td>Recognizes over 20 layout elements<\/td><\/tr>
Supported Versions<\/td>Limited<\/td>All HWP versions 3.0 and above<\/td><\/tr><\/tbody><\/table><\/figure>\n\n
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OCR and RAG Connection: Why Extraction Quality Determines Search Accuracy<\/strong><\/h3>\n\n

Document extraction quality determines the upper limit of the overall performance of an RAG system.<\/strong><\/p>\n\n

RAG operates in the following sequence: document extraction \u2192 chunking \u2192 embedding \u2192 vector DB storage \u2192 search \u2192 LLM response. If the extraction quality in the initial stage is low, all subsequent stages will be sequentially degraded. Flat text without structure is difficult to divide into meaningful units (chunking), and if incorrectly divided chunks are searched, the AI loses context and generates irrelevant answers. <\/p>\n\n

Hancom Data Loader’s Level inference automatically categorizes paragraph hierarchies by analyzing indentation, numbering schemes, and font sizes. Since hierarchical information is included in the structured data output, AI can accurately grasp context and provide answers in the subsequent chunking stage. Hancom Data Loader handles the extraction and parsing stages, and from the chunking stage onwards, it can be configured in conjunction with RAG solutions like Hancom Pedia. <\/p>\n\n

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\"Image<\/figure>\n\n
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Types of OCR Programs and Selection Criteria<\/strong><\/h2>\n\n

Comparison of OCR Programs: From Open Source to Enterprise Solutions<\/strong><\/h3>\n\n

OCR programs are categorized into three types: open source, cloud API, and on-premise<\/strong>. The appropriate method depends on document type, security environment, and infrastructure conditions. <\/p>\n\n

Open-source programs like Tesseract and PaddleOCR are free tools based on the Apache 2.0 license, supporting over 100 languages. However, as they operate image-based, they cannot directly process HWP\u00b7HWPX file formats. Cloud APIs such as Google Vision and AWS Textract offer quick adoption and high general accuracy, but require documents to be sent to external servers, making them unsuitable for public and financial institutions in closed network environments. The SaaS API method can be used immediately without separate installation, allowing selection based on security environment and implementation objectives. <\/strong><\/p>\n\n

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Overview of Open Source, Cloud API, and On-Premise Comparison<\/strong><\/h4>\n\n
Category<\/strong><\/td>Representative Tools<\/strong><\/td>Features<\/strong><\/td>Key Limitations<\/strong><\/td><\/tr>
Open Source<\/td>Tesseract, PaddleOCR<\/td>Free, customizable<\/td>Does not support HWP\u00b7HWPX file formats<\/td><\/tr>
Cloud API<\/td>Google Vision, AWS Textract<\/td>Quick adoption, high general accuracy<\/td>External transmission required \u2192 Not suitable for closed networks<\/td><\/tr>
On-Premise<\/td>Hancom Data Loader, etc.<\/td>Internal network installation, maximized security<\/td>Requires initial environment setup<\/td><\/tr><\/tbody><\/table><\/figure>\n\n

Before adoption, first check four criteria: whether HWP\u00b7HWPX processing is needed, whether it’s a closed network environment, whether it can run on CPU only, and whether AI pipeline integration is required<\/strong>. If two or more apply, you should consider a structured document parsing solution. <\/p>\n\n

\ud83d\udca1 If two or more of the above items apply, check out the actual implementation through the Hancom Data Loader demo. <\/p>\n\n

\ud83d\udc49 Go to Hancom Data Loader Live Demo<\/a> <\/strong><\/p>\n\n

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OCR FAQ (Frequently Asked Questions)<\/strong><\/h2>\n
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Q1. Are OCR and document parsing the same? <\/strong><\/h3>\n
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They are different technologies. OCR is the stage of reading characters in an image as text, while parsing (Document Parsing) is the stage of analyzing the structure and hierarchy of that text. For complete document automation, these two stages work together sequentially. <\/p>\n\n<\/div>\n<\/div>\n

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Q2. Can HWP\u00b7HWPX files be processed with OCR? <\/strong><\/h3>\n
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HWP\u00b7HWPX electronic documents require direct parsing, not OCR. Since HWP\u00b7HWPX files already have structural information digitally encoded, applying OCR necessitates prior PDF conversion, which results in the loss of formatting and table structures. Direct parsing based on HWP SDK core technology is the method that can process them without losing formatting and table structures. <\/p>\n\n<\/div>\n<\/div>\n

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Q3. Does OCR quality affect AI search accuracy? <\/strong><\/h3>\n
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It directly affects it. If the document extraction quality in the initial stage is low, even with excellent AI models, there will be limitations in search accuracy and answer quality. Text with a broken structure is difficult to divide into meaningful units, and the AI cannot find the correct context. <\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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\"Image<\/a><\/figure>\n\n
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Have you considered document structuring beyond OCR?<\/strong><\/h2>\n\n

\ud83d\udda5\ufe0f Hancom Data Loader<\/strong><\/a><\/h3>\n\n

To complete document automation that started with OCR, structured extraction that handles table structures, paragraph hierarchies, and HWP\u00b7HWPX formats is also necessary.<\/p>\n\n

Hancom Data Loader<\/a> internalizes Document Layout Analysis (DLA), OCR, and Table Structure Recognition (TSR) into a single pipeline to extract HWP, HWPX, PDF, and OOXML into structured data without conversion<\/strong>. It supports on-premise installation in closed networks and allows flexible operation of CPU and GPU. <\/p>\n\n

If you are considering building an RAG pipeline, start with Hancom Data Loader.<\/p>\n\n

\ud83d\udc49Inquire about Hancom Data Loader Adoption<\/a><\/strong><\/p>\n\n

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References<\/strong><\/p>\n\n

    \n
  1. IBM<\/strong><\/a>, “What is Optical Character Recognition (OCR)?”<\/li>\n\n\n\n
  2. AWS<\/strong><\/a>, “What is OCR?”<\/li>\n\n\n\n
  3. Idenfo<\/strong><\/a>, “The Evolution of OCR Technology: Past, Present, and Future”<\/li>\n\n\n\n
  4. Parseur<\/strong><\/a>, “What is OCR (Optical Character Recognition)?”<\/li>\n\n\n\n
  5. arXiv<\/strong><\/a>, “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, 2020<\/li>\n\n\n\n
  6. Tesseract OCR Official GitHub<\/strong><\/a><\/li>\n\n\n\n
  7. Amazon Textract Official API Documentation<\/strong><\/a><\/li>\n<\/ol>\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    What is OCR? From its meaning, principles, and types to AI OCR core technologies (DLA\u00b7TSR\u00b7VLM), HWP\u00b7HWPX processing methods, and the connection between OCR and RAG. We’ve summarized essential information to review before building a document automation pipeline. <\/p>\n","protected":false},"author":2,"featured_media":497,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[],"class_list":["post-496","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ax-glossary"],"_links":{"self":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/496","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=496"}],"version-history":[{"count":13,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/496\/revisions"}],"predecessor-version":[{"id":908,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/posts\/496\/revisions\/908"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/media\/497"}],"wp:attachment":[{"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/media?parent=496"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/categories?post=496"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.hancom.com\/en\/wp-json\/wp\/v2\/tags?post=496"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}