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| | <h1 class="title"><span class="chapter-number">2</span> <span class="chapter-title">Structured Document Processing</span></h1> |
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| | <section id="the-challenge" class="level2" data-number="2.1"> |
| | <h2 data-number="2.1" class="anchored" data-anchor-id="the-challenge"><span class="header-section-number">2.1</span> The Challenge</h2> |
| | <p>Many GLAM institutions have vast collections of structured documents—index cards, forms, registers—containing valuable information locked in physical or image formats. Manual transcription doesn’t scale, but the structured nature of these documents makes them ideal candidates for AI-powered processing.</p> |
| | <p>Unlocking this data means better discovery, new research possibilities, and integration with modern cataloguing systems.</p> |
| | </section> |
| | <section id="dont-we-just-need-ocr" class="level2" data-number="2.2"> |
| | <h2 data-number="2.2" class="anchored" data-anchor-id="dont-we-just-need-ocr"><span class="header-section-number">2.2</span> Don’t we just need OCR?</h2> |
| | <p>Traditional OCR extracts text from images, but that’s only half the problem. Consider an index card with a name, date, reference number, and description arranged in specific positions. OCR gives you a block of text—but not which part is the name, which is the date, or how they relate.</p> |
| | <p>Often, you don’t even need the raw text—you need the <em>information</em> it contains. A catalogue record doesn’t need “Mr. John Smith, 1847” preserved exactly; it needs <code>name: "John Smith"</code> and <code>year: 1847</code> as usable data.</p> |
| | <p>With OCR alone, you still need someone to parse text into structured fields. For hundreds of documents, that’s manageable. For hundreds of thousands, it’s not.</p> |
| | </section> |
| | <section id="solution-overview" class="level2" data-number="2.3"> |
| | <h2 data-number="2.3" class="anchored" data-anchor-id="solution-overview"><span class="header-section-number">2.3</span> Solution Overview</h2> |
| | <p>Structured extraction is a pattern that works across modalities—text, images, audio transcripts. The core idea is the same: constrain a model to return data in a predefined schema rather than freeform text.</p> |
| | <p>For document images, we use Vision Language Models (VLMs). Unlike OCR, VLMs understand both visual layout and textual content together. They can see that “1847” appears in the date field position, not just that the characters “1847” exist somewhere on the page.</p> |
| | <p>Structured output generation constrains the model to return your fields, your format. The result: input in, structured JSON out.</p> |
| | <p>This section focuses on the image case—extracting from document images—but the same principles apply when working with text or other formats.</p> |
| | <section id="what-this-pattern-looks-like" class="level3" data-number="2.3.1"> |
| | <h3 data-number="2.3.1" class="anchored" data-anchor-id="what-this-pattern-looks-like"><span class="header-section-number">2.3.1</span> What this pattern looks like</h3> |
| | <div class="cell" data-layout-align="default"> |
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| | <div> |
| | <p></p><figure class="figure"><p></p> |
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| | <pre class="mermaid mermaid-js">flowchart LR |
| | A[Document Image] --> B[VLM + Schema] |
| | B --> C[Structured JSON] |
| | C --> D[Catalogue/Database] |
| | </pre> |
| | </div> |
| | <p></p></figure><p></p> |
| | </div> |
| | </div> |
| | </div> |
| | <p>The following chapters walk through this in detail—starting with basic VLM queries, then building to real extraction workflows with evaluation strategies.</p> |
| | </section> |
| | </section> |
| | <section id="when-to-use-this-pattern" class="level2" data-number="2.4"> |
| | <h2 data-number="2.4" class="anchored" data-anchor-id="when-to-use-this-pattern"><span class="header-section-number">2.4</span> When to Use This Pattern</h2> |
| | <p><strong>Good fit:</strong></p> |
| | <ul> |
| | <li>Forms, index cards, registers with consistent layouts</li> |
| | <li>Documents where you know what fields you want to extract</li> |
| | <li>Collections too large for manual transcription</li> |
| | </ul> |
| | <p><strong>Less suited:</strong></p> |
| | <ul> |
| | <li>Free-form manuscripts with no predictable structure</li> |
| | <li>Documents requiring deep contextual interpretation</li> |
| | <li>Cases where verbatim transcription is the goal (use OCR instead)</li> |
| | </ul> |
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| | hash = url; |
| | } else { |
| | try { hash = new URL(url).hash; } catch {} |
| | } |
| | if (hash) { |
| | const id = hash.replace(/^#\/?/, ""); |
| | const note = window.document.getElementById(id); |
| | if (note !== null) { |
| | try { |
| | const html = processXRef(id, note.cloneNode(true)); |
| | instance.setContent(html); |
| | } finally { |
| | instance.enable(); |
| | instance.show(); |
| | } |
| | } else { |
| | |
| | fetch(url.split('#')[0]) |
| | .then(res => res.text()) |
| | .then(html => { |
| | const parser = new DOMParser(); |
| | const htmlDoc = parser.parseFromString(html, "text/html"); |
| | const note = htmlDoc.getElementById(id); |
| | if (note !== null) { |
| | const html = processXRef(id, note); |
| | instance.setContent(html); |
| | } |
| | }).finally(() => { |
| | instance.enable(); |
| | instance.show(); |
| | }); |
| | } |
| | } else { |
| | |
| | |
| | fetch(url) |
| | .then(res => res.text()) |
| | .then(html => { |
| | const parser = new DOMParser(); |
| | const htmlDoc = parser.parseFromString(html, "text/html"); |
| | const note = htmlDoc.querySelector('main.content'); |
| | if (note !== null) { |
| | |
| | |
| | |
| | if (note.children.length > 0 && note.children[0].tagName === "HEADER") { |
| | note.children[0].remove(); |
| | } |
| | const html = processXRef(null, note); |
| | instance.setContent(html); |
| | } |
| | }).finally(() => { |
| | instance.enable(); |
| | instance.show(); |
| | }); |
| | } |
| | }, function(instance) { |
| | }); |
| | } |
| | let selectedAnnoteEl; |
| | const selectorForAnnotation = ( cell, annotation) => { |
| | let cellAttr = 'data-code-cell="' + cell + '"'; |
| | let lineAttr = 'data-code-annotation="' + annotation + '"'; |
| | const selector = 'span[' + cellAttr + '][' + lineAttr + ']'; |
| | return selector; |
| | } |
| | const selectCodeLines = (annoteEl) => { |
| | const doc = window.document; |
| | const targetCell = annoteEl.getAttribute("data-target-cell"); |
| | const targetAnnotation = annoteEl.getAttribute("data-target-annotation"); |
| | const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation)); |
| | const lines = annoteSpan.getAttribute("data-code-lines").split(","); |
| | const lineIds = lines.map((line) => { |
| | return targetCell + "-" + line; |
| | }) |
| | let top = null; |
| | let height = null; |
| | let parent = null; |
| | if (lineIds.length > 0) { |
| | |
| | const el = window.document.getElementById(lineIds[0]); |
| | top = el.offsetTop; |
| | height = el.offsetHeight; |
| | parent = el.parentElement.parentElement; |
| | if (lineIds.length > 1) { |
| | const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]); |
| | const bottom = lastEl.offsetTop + lastEl.offsetHeight; |
| | height = bottom - top; |
| | } |
| | if (top !== null && height !== null && parent !== null) { |
| | |
| | let div = window.document.getElementById("code-annotation-line-highlight"); |
| | if (div === null) { |
| | div = window.document.createElement("div"); |
| | div.setAttribute("id", "code-annotation-line-highlight"); |
| | div.style.position = 'absolute'; |
| | parent.appendChild(div); |
| | } |
| | div.style.top = top - 2 + "px"; |
| | div.style.height = height + 4 + "px"; |
| | div.style.left = 0; |
| | let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter"); |
| | if (gutterDiv === null) { |
| | gutterDiv = window.document.createElement("div"); |
| | gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter"); |
| | gutterDiv.style.position = 'absolute'; |
| | const codeCell = window.document.getElementById(targetCell); |
| | const gutter = codeCell.querySelector('.code-annotation-gutter'); |
| | gutter.appendChild(gutterDiv); |
| | } |
| | gutterDiv.style.top = top - 2 + "px"; |
| | gutterDiv.style.height = height + 4 + "px"; |
| | } |
| | selectedAnnoteEl = annoteEl; |
| | } |
| | }; |
| | const unselectCodeLines = () => { |
| | const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"]; |
| | elementsIds.forEach((elId) => { |
| | const div = window.document.getElementById(elId); |
| | if (div) { |
| | div.remove(); |
| | } |
| | }); |
| | selectedAnnoteEl = undefined; |
| | }; |
| | |
| | window.addEventListener( |
| | "resize", |
| | throttle(() => { |
| | elRect = undefined; |
| | if (selectedAnnoteEl) { |
| | selectCodeLines(selectedAnnoteEl); |
| | } |
| | }, 10) |
| | ); |
| | function throttle(fn, ms) { |
| | let throttle = false; |
| | let timer; |
| | return (...args) => { |
| | if(!throttle) { |
| | fn.apply(this, args); |
| | throttle = true; |
| | } else { |
| | if(timer) clearTimeout(timer); |
| | timer = setTimeout(() => { |
| | fn.apply(this, args); |
| | timer = throttle = false; |
| | }, ms); |
| | } |
| | }; |
| | } |
| | |
| | const annoteDls = window.document.querySelectorAll('dt[data-target-cell]'); |
| | for (const annoteDlNode of annoteDls) { |
| | annoteDlNode.addEventListener('click', (event) => { |
| | const clickedEl = event.target; |
| | if (clickedEl !== selectedAnnoteEl) { |
| | unselectCodeLines(); |
| | const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active'); |
| | if (activeEl) { |
| | activeEl.classList.remove('code-annotation-active'); |
| | } |
| | selectCodeLines(clickedEl); |
| | clickedEl.classList.add('code-annotation-active'); |
| | } else { |
| | |
| | unselectCodeLines(); |
| | clickedEl.classList.remove('code-annotation-active'); |
| | } |
| | }); |
| | } |
| | const findCites = (el) => { |
| | const parentEl = el.parentElement; |
| | if (parentEl) { |
| | const cites = parentEl.dataset.cites; |
| | if (cites) { |
| | return { |
| | el, |
| | cites: cites.split(' ') |
| | }; |
| | } else { |
| | return findCites(el.parentElement) |
| | } |
| | } else { |
| | return undefined; |
| | } |
| | }; |
| | var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]'); |
| | for (var i=0; i<bibliorefs.length; i++) { |
| | const ref = bibliorefs[i]; |
| | const citeInfo = findCites(ref); |
| | if (citeInfo) { |
| | tippyHover(citeInfo.el, function() { |
| | var popup = window.document.createElement('div'); |
| | citeInfo.cites.forEach(function(cite) { |
| | var citeDiv = window.document.createElement('div'); |
| | citeDiv.classList.add('hanging-indent'); |
| | citeDiv.classList.add('csl-entry'); |
| | var biblioDiv = window.document.getElementById('ref-' + cite); |
| | if (biblioDiv) { |
| | citeDiv.innerHTML = biblioDiv.innerHTML; |
| | } |
| | popup.appendChild(citeDiv); |
| | }); |
| | return popup.innerHTML; |
| | }); |
| | } |
| | } |
| | }); |
| | </script> |
| | <nav class="page-navigation"> |
| | <div class="nav-page nav-page-previous"> |
| | <a href="../../patterns/what-is-an-ai-pattern.html" class="pagination-link" aria-label="What is an AI Pattern?"> |
| | <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text"><span class="chapter-number">1</span> <span class="chapter-title">What is an AI Pattern?</span></span> |
| | </a> |
| | </div> |
| | <div class="nav-page nav-page-next"> |
| | <a href="../../patterns/structured-generation/vlm-structured-generation.html" class="pagination-link" aria-label="Structured Information Extraction with Vision Language Models"> |
| | <span class="nav-page-text"><span class="chapter-number">3</span> <span class="chapter-title">Structured Information Extraction with Vision Language Models</span></span> <i class="bi bi-arrow-right-short"></i> |
| | </a> |
| | </div> |
| | </nav> |
| | </div> |
| |
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| | </body></html> |
