Scanning physical documents with a phone or laptop camera—and getting a clean, de-skewed result instantly—is a common requirement in expense-management, healthcare, and contract-signing workflows. The Dynamsoft Capture Vision SDK handles the heavy lifting (boundary detection, perspective normalization) entirely in the browser via WebAssembly, so no server-side component is needed.
What you'll build: A vanilla-JavaScript web app that opens the device camera, automatically detects document boundaries frame-by-frame, captures and normalizes multi-page documents, lets users edit corners, apply image filters, reorder pages, and export the result as a PDF or individual PNGs — powered by Dynamsoft Capture Vision Bundle v3.2.5000.
Online Demo
https://yushulx.me/javascript-barcode-qr-code-scanner/examples/multi-document-capture/
Demo Video: Multi-Page Document Scanner in Action
Prerequisites
- A modern browser with camera access (Chrome, Edge, Safari, Firefox).
- Node.js and any static HTTP server (Python's
http.server,npx serve, etc.) for local development. - A Dynamsoft Capture Vision license key.
Get a 30-day free trial license for Dynamsoft Capture Vision.
Step 1: Load the SDK via CDN
The entire SDK is delivered as a single bundle from jsDelivr — no npm install required. Add both the Dynamsoft Capture Vision bundle and jsPDF to the <head> of index.html:
<script src="https://cdn.jsdelivr.net/npm/dynamsoft-capture-vision-bundle@3.2.5000/dist/dcv.bundle.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/jspdf@2.5.1/dist/jspdf.umd.min.js"></script>
The bundle includes the Document Detection & Normalization (DDN) module, the CaptureVisionRouter, and the LicenseManager — everything needed to go from raw camera frames to normalized document images.
Step 2: Initialize the License and CaptureVisionRouter
After the user submits a license key on the start screen, initSDK activates the license, loads the DDN WebAssembly module, creates a CaptureVisionRouter instance, and loads the preset template file that defines the detection and normalization tasks:
const TEMPLATE_PATH = "./DBR_and_DDN_detect_PresetTemplates.json";
const DETECT_TEMPLATE = "DetectDocumentBoundaries_Default";
const NORMALIZE_TEMPLATE = "NormalizeDocument_Default";
async function initSDK(licenseKey) {
updateInitStatus("Activating license...");
await Dynamsoft.License.LicenseManager.initLicense(licenseKey, true);
updateInitStatus("Loading modules...");
await Dynamsoft.Core.CoreModule.loadWasm(["DDN"]);
updateInitStatus("Initializing scanner...");
cvr = await Dynamsoft.CVR.CaptureVisionRouter.createInstance();
await cvr.initSettings(TEMPLATE_PATH);
updateInitStatus("Opening camera...");
await initCamera();
}
The initSettings call registers DetectDocumentBoundaries_Default and NormalizeDocument_Default as named tasks so they can be invoked by name later.
Step 3: Run the Frame-by-Frame Detection Loop
👁 real-time document detection with camera
The scan loop calls cvr.capture() on each animation frame, passing the live <video> element and the detection template name. When a four-point quad is returned, it is drawn on an overlay <canvas> and fed into the stabilizer:
async function scanLoop() {
if (!isScanning) return;
if (isProcessingFrame || isCaptureInProgress) {
scanLoopId = requestAnimationFrame(scanLoop);
return;
}
isProcessingFrame = true;
try {
const result = await cvr.capture(videoElement, DETECT_TEMPLATE);
let quad = null;
for (const item of result.items || []) {
if (item.location && item.location.points && item.location.points.length === 4) {
quad = item.location.points;
break;
}
}
if (quad) {
latestDetectedQuad = quad;
drawOverlay(quad);
// ... stabilizer logic and auto-capture
} else {
clearOverlay();
resetStabilizer();
}
} catch (_) {
clearOverlay();
}
isProcessingFrame = false;
if (isScanning) {
scanLoopId = requestAnimationFrame(scanLoop);
}
}
The isProcessingFrame guard prevents concurrent captures on devices where WebAssembly inference takes longer than a single animation frame.
Step 4: Stabilize the Detected Quad Before Auto-Capture
Firing a capture the instant a quad is detected produces blurry or mis-framed results. The stabilizer compares consecutive quads using Intersection over Union (IoU) and an area-delta check; only after stableFrameCount consecutive stable frames is the capture triggered automatically:
const quadStabilizer = {
enabled: true,
iouThreshold: 0.85,
areaDeltaThreshold: 0.15,
stableFrameCount: 3,
};
function isQuadStable(current, previous) {
if (!current || !previous || current.length !== 4 || previous.length !== 4) return false;
const boxA = pointsToBoundingBox(current);
const boxB = pointsToBoundingBox(previous);
const iou = computeIoU(boxA, boxB);
const areaA = polygonArea(current);
const areaB = polygonArea(previous);
const areaDelta = areaB === 0 ? 1 : Math.abs(areaA - areaB) / areaB;
return iou >= quadStabilizer.iouThreshold && areaDelta <= quadStabilizer.areaDeltaThreshold;
}
All three thresholds (iouThreshold, areaDeltaThreshold, stableFrameCount) are exposed in an in-app settings panel so users can tune them for their environment.
Step 5: Normalize the Document with Perspective Correction
Once a stable quad is confirmed (or the user taps the capture button), normalizeDocument projects the detected boundary points back into cvr to perform perspective de-skew and returns the corrected image as a <canvas>:
async function normalizeDocument(frameCanvas, points) {
try {
const settings = await cvr.getSimplifiedSettings(NORMALIZE_TEMPLATE);
settings.roi.points = points;
settings.roiMeasuredInPercentage = 0;
await cvr.updateSettings(NORMALIZE_TEMPLATE, settings);
const normalizedResult = await cvr.capture(frameCanvas, NORMALIZE_TEMPLATE);
for (const item of normalizedResult.items || []) {
if (item.toCanvas && typeof item.toCanvas === "function") {
return item.toCanvas();
}
}
return null;
} catch (_) {
return null;
}
}
Setting roiMeasuredInPercentage = 0 tells the router that settings.roi.points are pixel coordinates in the source frame, not percentages.
Step 6: Apply Image Filters Per Page
After capture, each page can be rendered in color, grayscale, or binary mode. The filter is applied client-side using getImageData / putImageData on a scratch canvas, leaving the original baseCanvas untouched:
function buildFilteredCanvas(page) {
const src = page.baseCanvas;
const out = document.createElement("canvas");
out.width = src.width;
out.height = src.height;
const ctx = out.getContext("2d", { willReadFrequently: true });
ctx.drawImage(src, 0, 0);
if (page.filter === "color") {
return out;
}
const image = ctx.getImageData(0, 0, out.width, out.height);
const data = image.data;
for (let i = 0; i < data.length; i += 4) {
const gray = Math.round(data[i] * 0.299 + data[i + 1] * 0.587 + data[i + 2] * 0.114);
if (page.filter === "grayscale") {
data[i] = gray;
data[i + 1] = gray;
data[i + 2] = gray;
} else {
const binary = gray > 140 ? 255 : 0;
data[i] = binary;
data[i + 1] = binary;
data[i + 2] = binary;
}
}
ctx.putImageData(image, 0, 0);
return out;
}
Step 7: Edit the Document Quad After Capture
If the auto-detected corners are inaccurate, the user can open the edit screen for any captured page, drag the four corner handles to the correct positions, and re-apply perspective normalization without recapturing. openEdit loads the stored originalCanvas and quadPoints into editState, then renderEditCanvas draws the image and overlays draggable 14 px circular handles:
function openEdit() {
const page = pages[currentPageIndex];
if (!page.originalCanvas || !page.quadPoints) {
showToast("No quad data available for editing.");
return;
}
editState = {
originalCanvas: page.originalCanvas,
quadPoints: page.quadPoints.map(p => ({ x: p.x, y: p.y })),
draggingCorner: -1,
imgRect: null,
};
resultScreen.classList.add("hidden");
editScreen.classList.remove("hidden");
requestAnimationFrame(renderEditCanvas);
}
function renderEditCanvas() {
if (!editState) return;
const rect = editCanvasEl.getBoundingClientRect();
const cw = Math.round(rect.width);
const ch = Math.round(rect.height);
if (cw === 0 || ch === 0) return;
editCanvasEl.width = cw;
editCanvasEl.height = ch;
const img = editState.originalCanvas;
const scale = Math.min(cw / img.width, ch / img.height);
const imgW = Math.round(img.width * scale);
const imgH = Math.round(img.height * scale);
const imgX = Math.round((cw - imgW) / 2);
const imgY = Math.round((ch - imgH) / 2);
editState.imgRect = { x: imgX, y: imgY, w: imgW, h: imgH };
editCtx2.clearRect(0, 0, cw, ch);
editCtx2.drawImage(img, imgX, imgY, imgW, imgH);
const pts = editState.quadPoints.map(p => ({
x: imgX + (p.x / img.width) * imgW,
y: imgY + (p.y / img.height) * imgH,
}));
editCtx2.beginPath();
editCtx2.moveTo(pts[0].x, pts[0].y);
for (let i = 1; i < 4; i++) editCtx2.lineTo(pts[i].x, pts[i].y);
editCtx2.closePath();
editCtx2.fillStyle = "rgba(106,196,187,0.2)";
editCtx2.fill();
editCtx2.strokeStyle = "#6ac4bb";
editCtx2.lineWidth = 2;
editCtx2.stroke();
const HANDLE_R = 14;
pts.forEach((p, i) => {
editCtx2.beginPath();
editCtx2.arc(p.x, p.y, HANDLE_R, 0, Math.PI * 2);
editCtx2.fillStyle = i === editState.draggingCorner ? "#fe8e14" : "#6ac4bb";
editCtx2.fill();
editCtx2.strokeStyle = "#fff";
editCtx2.lineWidth = 2;
editCtx2.stroke();
});
}
When the user taps Apply, applyEdit calls normalizeDocument with the updated corner coordinates and replaces the stored baseCanvas in-place — the page is corrected without affecting any other page:
async function applyEdit() {
if (!editState) return;
editApplyBtn.disabled = true;
try {
const normalized = await normalizeDocument(editState.originalCanvas, editState.quadPoints);
if (!normalized) {
showToast("Normalization failed. Adjust corners and try again.");
return;
}
pages[currentPageIndex].baseCanvas = copyCanvas(normalized);
pages[currentPageIndex].quadPoints = editState.quadPoints.map(p => ({ x: p.x, y: p.y }));
editState = null;
editScreen.classList.add("hidden");
resultScreen.classList.remove("hidden");
renderResult();
updateThumbnailBar();
} catch (e) {
console.error(e);
showToast("Edit failed: " + (e?.message || "Unknown error"));
} finally {
editApplyBtn.disabled = false;
}
}
Step 8: Reorder Pages with Drag-and-Drop
For multi-page sessions, the sort overlay lets users drag thumbnail cards into the desired order. The implementation supports both mouse (dragstart / drop) and touch (touchstart / touchmove / touchend) events. A floating clone follows the finger during a touch drag, and document.elementFromPoint identifies the drop target:
function openSort() {
if (pages.length < 2) {
showToast("Need at least 2 pages to reorder.");
return;
}
const workingOrder = pages.map((_, i) => i);
let dragIdx = -1;
function rebuild() {
sortList.innerHTML = "";
workingOrder.forEach((pageIdx, pos) => {
const item = document.createElement("div");
item.className = "sort-item";
item.draggable = true;
item.dataset.pos = pos;
item.addEventListener("dragstart", (e) => {
dragIdx = pos;
item.classList.add("dragging");
e.dataTransfer.effectAllowed = "move";
});
item.addEventListener("drop", (e) => {
e.preventDefault();
item.classList.remove("drag-over");
const dropPos = Number(item.dataset.pos);
if (dragIdx >= 0 && dragIdx !== dropPos) {
const [moved] = workingOrder.splice(dragIdx, 1);
workingOrder.splice(dropPos, 0, moved);
rebuild();
}
});
// Touch drag support
let touchClone = null;
item.addEventListener("touchstart", (e) => {
dragIdx = pos;
const touch = e.touches[0];
touchClone = item.cloneNode(true);
touchClone.style.cssText = `position:fixed;left:${touch.clientX - 30}px;top:${touch.clientY - 24}px;width:${item.offsetWidth}px;opacity:0.8;z-index:200;pointer-events:none`;
document.body.appendChild(touchClone);
item.classList.add("dragging");
}, { passive: true });
item.addEventListener("touchend", (e) => {
item.classList.remove("dragging");
if (touchClone) { touchClone.remove(); touchClone = null; }
const touch = e.changedTouches[0];
const overItem = document.elementFromPoint(touch.clientX, touch.clientY)?.closest(".sort-item");
if (overItem) {
const dropPos = Number(overItem.dataset.pos);
if (dragIdx >= 0 && dragIdx !== dropPos) {
const [moved] = workingOrder.splice(dragIdx, 1);
workingOrder.splice(dropPos, 0, moved);
rebuild();
}
}
dragIdx = -1;
});
sortList.appendChild(item);
});
}
rebuild();
sortOverlay.classList.remove("hidden");
sortDoneBtn.onclick = () => {
pages = workingOrder.map(i => pages[i]);
currentPageIndex = 0;
sortOverlay.classList.add("hidden");
renderResult();
updateThumbnailBar();
};
}
When the user taps Done, workingOrder (an index remapping array) is applied to pages in a single .map() call, making the reorder non-destructive until confirmed.
Step 9: Export the Scanned Pages as a PDF
👁 Multi-Page Document Scanner Web App
All captured pages (with filters applied) are packed into a single multi-page PDF using jsPDF. Each page is centered and scaled to fit A4:
async function exportPdf() {
if (!pages.length || !window.jspdf) return;
const { jsPDF } = window.jspdf;
const pdf = new jsPDF({ unit: "pt", format: "a4" });
for (let i = 0; i < pages.length; i++) {
const canvas = buildFilteredCanvas(pages[i]);
const imageData = canvas.toDataURL("image/jpeg", 0.95);
if (i > 0) pdf.addPage("a4", "portrait");
const pageWidth = pdf.internal.pageSize.getWidth();
const pageHeight = pdf.internal.pageSize.getHeight();
const ratio = Math.min(pageWidth / canvas.width, pageHeight / canvas.height);
const drawWidth = canvas.width * ratio;
const drawHeight = canvas.height * ratio;
const x = (pageWidth - drawWidth) / 2;
const y = (pageHeight - drawHeight) / 2;
pdf.addImage(imageData, "JPEG", x, y, drawWidth, drawHeight);
}
const blob = pdf.output("blob");
const blobUrl = URL.createObjectURL(blob);
window.open(blobUrl, "_blank", "noopener,noreferrer");
}
Individual pages can also be downloaded as PNG files via the Export as Images option, which calls canvas.toDataURL("image/png") for each page.
Source Code
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