{"version":3,"file":"index.mjs","names":[],"sources":["../src/utils/math.ts","../src/io/canvas.ts","../src/runtime/ort.ts","../src/runtime/provider.ts","../src/pipeline/detect.ts","../src/pipeline/preprocess.ts","../src/pipeline/recognize.ts","../src/paddle-ocr.ts","../src/manifests/official.ts","../src/presets/ppocrv5.ts"],"sourcesContent":["import type { OcrBox } from \"../types\";\n\nexport function clamp(value: number, min: number, max: number): number {\n return Math.min(max, Math.max(min, value));\n}\n\nexport function roundUpToMultiple(value: number, multiple: number): number {\n return Math.max(multiple, Math.ceil(value / multiple) * multiple);\n}\n\nexport function sortBoxes(boxes: readonly OcrBox[]): OcrBox[] {\n return [...boxes].sort((left, right) => {\n const ly = left.points[0].y;\n const ry = right.points[0].y;\n if (Math.abs(ly - ry) > 12) {\n return ly - ry;\n }\n\n return left.points[0].x - right.points[0].x;\n });\n}\n\nexport function axisAlignedBounds(box: OcrBox): {\n left: number;\n top: number;\n width: number;\n height: number;\n} {\n const xs = box.points.map((point) => point.x);\n const ys = box.points.map((point) => point.y);\n const left = Math.min(...xs);\n const right = Math.max(...xs);\n const top = Math.min(...ys);\n const bottom = Math.max(...ys);\n\n return {\n left,\n top,\n width: right - left,\n height: bottom - top\n };\n}\n\nexport function maxIndex(values: ArrayLike): { index: number; value: number } {\n let index = 0;\n let value = values[0] ?? Number.NEGATIVE_INFINITY;\n\n for (let cursor = 1; cursor < values.length; cursor += 1) {\n if (values[cursor] > value) {\n index = cursor;\n value = values[cursor];\n }\n }\n\n return { index, value };\n}\n\nexport function maxProbability(values: ArrayLike): number {\n let min = Number.POSITIVE_INFINITY;\n let max = Number.NEGATIVE_INFINITY;\n\n for (let cursor = 0; cursor < values.length; cursor += 1) {\n const value = values[cursor];\n if (value < min) {\n min = value;\n }\n if (value > max) {\n max = value;\n }\n }\n\n if (min >= 0 && max <= 1) {\n return max;\n }\n\n let total = 0;\n for (let cursor = 0; cursor < values.length; cursor += 1) {\n total += Math.exp(values[cursor] - max);\n }\n\n return 1 / total;\n}\n","import type { OcrImageSource } from \"../types\";\nimport { clamp } from \"../utils/math\";\n\ntype AnyCanvas = HTMLCanvasElement | OffscreenCanvas;\ntype AnyRenderingContext = CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D;\n\nfunction hasOffscreenCanvas(): boolean {\n return typeof OffscreenCanvas !== \"undefined\";\n}\n\nfunction isImageData(value: unknown): value is ImageData {\n return typeof ImageData !== \"undefined\" && value instanceof ImageData;\n}\n\nfunction isImageBitmap(value: unknown): value is ImageBitmap {\n return typeof ImageBitmap !== \"undefined\" && value instanceof ImageBitmap;\n}\n\nfunction isHtmlImageElement(value: unknown): value is HTMLImageElement {\n return typeof HTMLImageElement !== \"undefined\" && value instanceof HTMLImageElement;\n}\n\nfunction isHtmlCanvasElement(value: unknown): value is HTMLCanvasElement {\n return typeof HTMLCanvasElement !== \"undefined\" && value instanceof HTMLCanvasElement;\n}\n\nfunction isOffscreenCanvas(value: unknown): value is OffscreenCanvas {\n return hasOffscreenCanvas() && value instanceof OffscreenCanvas;\n}\n\nfunction createCanvas(width: number, height: number): AnyCanvas {\n if (hasOffscreenCanvas()) {\n return new OffscreenCanvas(width, height);\n }\n\n if (typeof document !== \"undefined\") {\n const canvas = document.createElement(\"canvas\");\n canvas.width = width;\n canvas.height = height;\n return canvas;\n }\n\n throw new Error(\"A canvas implementation is required in this runtime.\");\n}\n\nfunction getContext(canvas: AnyCanvas): AnyRenderingContext {\n const context = canvas.getContext(\"2d\", { willReadFrequently: true });\n if (!context) {\n throw new Error(\"Unable to create a 2D canvas context.\");\n }\n\n return context;\n}\n\nasync function loadImageFromUrl(url: string): Promise {\n if (typeof createImageBitmap !== \"undefined\") {\n const response = await fetch(url);\n if (!response.ok) {\n throw new Error(`Failed to fetch image from ${url}`);\n }\n const blob = await response.blob();\n return createImageBitmap(blob);\n }\n\n if (typeof Image === \"undefined\") {\n throw new Error(\"This runtime cannot load URL inputs.\");\n }\n\n return await new Promise((resolve, reject) => {\n const image = new Image();\n image.crossOrigin = \"anonymous\";\n image.onload = () => resolve(image);\n image.onerror = () => reject(new Error(`Failed to load image from ${url}`));\n image.src = url;\n });\n}\n\nasync function blobToImage(blob: Blob): Promise {\n if (typeof createImageBitmap !== \"undefined\") {\n return createImageBitmap(blob);\n }\n\n const url = URL.createObjectURL(blob);\n try {\n return await loadImageFromUrl(url);\n } finally {\n URL.revokeObjectURL(url);\n }\n}\n\nfunction drawIntoImageData(source: ImageBitmap | HTMLImageElement): ImageData {\n const width = isHtmlImageElement(source) ? source.naturalWidth || source.width : source.width;\n const height = isHtmlImageElement(source) ? source.naturalHeight || source.height : source.height;\n const canvas = createCanvas(width, height);\n const context = getContext(canvas);\n context.drawImage(source, 0, 0, width, height);\n return context.getImageData(0, 0, width, height);\n}\n\nexport async function ensureImageData(source: OcrImageSource): Promise {\n if (isImageData(source)) {\n return source;\n }\n\n if (isHtmlCanvasElement(source) || isOffscreenCanvas(source)) {\n const context = getContext(source);\n return context.getImageData(0, 0, source.width, source.height);\n }\n\n if (isImageBitmap(source) || isHtmlImageElement(source)) {\n return drawIntoImageData(source);\n }\n\n if (typeof source === \"string\") {\n return drawIntoImageData(await loadImageFromUrl(source));\n }\n\n if (source instanceof Blob) {\n return drawIntoImageData(await blobToImage(source));\n }\n\n throw new Error(\"Unsupported image source.\");\n}\n\nexport function resizeImageData(source: ImageData, width: number, height: number): ImageData {\n const inputCanvas = createCanvas(source.width, source.height);\n const inputContext = getContext(inputCanvas);\n inputContext.putImageData(source, 0, 0);\n\n const outputCanvas = createCanvas(width, height);\n const outputContext = getContext(outputCanvas);\n outputContext.drawImage(inputCanvas, 0, 0, width, height);\n\n return outputContext.getImageData(0, 0, width, height);\n}\n\nexport function cropImageData(\n source: ImageData,\n left: number,\n top: number,\n width: number,\n height: number\n): ImageData {\n const x = clamp(Math.floor(left), 0, source.width - 1);\n const y = clamp(Math.floor(top), 0, source.height - 1);\n const w = clamp(Math.ceil(width), 1, source.width - x);\n const h = clamp(Math.ceil(height), 1, source.height - y);\n\n const pixels = new Uint8ClampedArray(w * h * 4);\n\n for (let row = 0; row < h; row += 1) {\n const sourceOffset = ((y + row) * source.width + x) * 4;\n const targetOffset = row * w * 4;\n pixels.set(source.data.subarray(sourceOffset, sourceOffset + w * 4), targetOffset);\n }\n\n return new ImageData(pixels, w, h);\n}\n","import * as ort from \"onnxruntime-web\";\nimport type { ExecutionProvider, OrtRuntimeOptions } from \"../types\";\n\nexport type OrtSession = ort.InferenceSession;\n\nexport type DownloadProgressCallback = (loaded: number, total: number | undefined) => void;\n\nlet configured = false;\n\nexport function configureOrt(options?: OrtRuntimeOptions): void {\n if (configured || !options) {\n return;\n }\n\n if (options.wasmPaths) {\n ort.env.wasm.wasmPaths = options.wasmPaths;\n }\n\n if (options.wasmThreads) {\n ort.env.wasm.numThreads = options.wasmThreads;\n }\n\n configured = true;\n}\n\nconst MODEL_CACHE_NAME = \"ffocr-models\";\n\nasync function readResponseWithProgress(\n response: Response,\n onProgress?: DownloadProgressCallback\n): Promise {\n if (!onProgress || !response.body) {\n return response.arrayBuffer();\n }\n\n const contentLength = response.headers.get(\"content-length\");\n const total = contentLength ? parseInt(contentLength, 10) : undefined;\n const reader = response.body.getReader();\n const chunks: Uint8Array[] = [];\n let loaded = 0;\n\n for (;;) {\n const { done, value } = await reader.read();\n if (done) break;\n chunks.push(value);\n loaded += value.byteLength;\n onProgress(loaded, total);\n }\n\n const result = new Uint8Array(loaded);\n let offset = 0;\n for (const chunk of chunks) {\n result.set(chunk, offset);\n offset += chunk.byteLength;\n }\n return result.buffer;\n}\n\nasync function fetchWithProgress(\n url: string,\n onProgress?: DownloadProgressCallback,\n useCache?: boolean\n): Promise {\n if (useCache && typeof caches !== \"undefined\") {\n const cache = await caches.open(MODEL_CACHE_NAME);\n const cached = await cache.match(url);\n if (cached) {\n return readResponseWithProgress(cached, onProgress);\n }\n\n const response = await fetch(url);\n if (!response.ok) {\n throw new Error(`Failed to fetch ${url}: ${response.status}`);\n }\n\n const cloned = response.clone();\n cache.put(url, cloned);\n return readResponseWithProgress(response, onProgress);\n }\n\n const response = await fetch(url);\n if (!response.ok) {\n throw new Error(`Failed to fetch ${url}: ${response.status}`);\n }\n return readResponseWithProgress(response, onProgress);\n}\n\nexport async function createSession(\n url: string,\n provider: ExecutionProvider,\n onDownloadProgress?: DownloadProgressCallback,\n useCache?: boolean\n): Promise {\n const data = await fetchWithProgress(url, onDownloadProgress, useCache);\n return ort.InferenceSession.create(data, {\n executionProviders: [provider],\n graphOptimizationLevel: \"all\"\n });\n}\n\nexport async function runSession(\n session: OrtSession,\n data: Float32Array,\n dims: readonly number[],\n inputName?: string,\n outputName?: string\n): Promise {\n const feeds: Record = {\n [inputName ?? session.inputNames[0]]: new ort.Tensor(\"float32\", data, dims)\n };\n\n const outputs = await session.run(feeds);\n const output = outputs[outputName ?? session.outputNames[0]];\n if (!output) {\n throw new Error(\"ONNX Runtime returned no matching output tensor.\");\n }\n\n return output;\n}\n","import type {\n ExecutionProvider,\n ExecutionProviderSupport,\n PaddleOcrModelManifest,\n ProviderPreference,\n RuntimeBenchmark\n} from \"../types\";\n\nconst STORAGE_PREFIX = \"ffocr:provider:\";\nconst EXECUTION_PROVIDER_PRIORITY: readonly ExecutionProvider[] = [\n \"webgpu\",\n \"webnn\",\n \"webgl\",\n \"wasm\"\n];\n\nfunction normalizePreference(preference: ProviderPreference | undefined): ExecutionProvider[] {\n if (!preference || preference === \"auto\") {\n const available = getAvailableExecutionProviders();\n return available.length > 0 ? available : [\"wasm\"];\n }\n\n if (typeof preference === \"string\") {\n return [preference];\n }\n\n return [...preference];\n}\n\nexport function getProviderCandidates(\n preference: ProviderPreference | undefined\n): ExecutionProvider[] {\n return normalizePreference(preference).filter(\n (value, index, array) => array.indexOf(value) === index\n );\n}\n\nexport function getKnownExecutionProviders(): readonly ExecutionProvider[] {\n return EXECUTION_PROVIDER_PRIORITY;\n}\n\nexport function getExecutionProviderSupport(): readonly ExecutionProviderSupport[] {\n return EXECUTION_PROVIDER_PRIORITY.map((provider) => ({\n provider,\n available: isExecutionProviderAvailable(provider)\n }));\n}\n\nexport function getAvailableExecutionProviders(): ExecutionProvider[] {\n return getExecutionProviderSupport()\n .filter((item) => item.available)\n .map((item) => item.provider);\n}\n\nexport function isExecutionProviderAvailable(provider: ExecutionProvider): boolean {\n switch (provider) {\n case \"webgpu\":\n return supportsWebGpu();\n case \"webnn\":\n return supportsWebNN();\n case \"webgl\":\n return supportsWebGl();\n case \"wasm\":\n return supportsWasm();\n default:\n return false;\n }\n}\n\nexport function supportsWebGpu(): boolean {\n return typeof navigator !== \"undefined\" && \"gpu\" in navigator;\n}\n\nexport function supportsWebNN(): boolean {\n return typeof navigator !== \"undefined\" && \"ml\" in navigator;\n}\n\nexport function supportsWebGl(): boolean {\n const canvas = createProbeCanvas();\n if (!canvas) {\n return false;\n }\n\n try {\n const standardContext = canvas.getContext(\"webgl2\") || canvas.getContext(\"webgl\");\n if (standardContext) {\n return true;\n }\n\n return typeof HTMLCanvasElement !== \"undefined\" && canvas instanceof HTMLCanvasElement\n ? Boolean(canvas.getContext(\"experimental-webgl\"))\n : false;\n } catch {\n return false;\n }\n}\n\nexport function supportsWasm(): boolean {\n return typeof WebAssembly !== \"undefined\";\n}\n\nexport function buildProviderCacheKey(manifest: PaddleOcrModelManifest): string {\n const userAgent = typeof navigator === \"undefined\" ? \"unknown\" : navigator.userAgent;\n return `${STORAGE_PREFIX}${manifest.id}:${userAgent}`;\n}\n\nexport function readCachedProvider(cacheKey: string): ExecutionProvider | null {\n if (typeof localStorage === \"undefined\") {\n return null;\n }\n\n const cached = localStorage.getItem(cacheKey);\n if (isExecutionProvider(cached)) {\n return cached;\n }\n\n return null;\n}\n\nfunction isExecutionProvider(value: string | null): value is ExecutionProvider {\n return value !== null && getKnownExecutionProviders().includes(value as ExecutionProvider);\n}\n\nexport function writeCachedProvider(\n cacheKey: string,\n provider: ExecutionProvider,\n benchmarks: readonly RuntimeBenchmark[]\n): void {\n if (typeof localStorage === \"undefined\") {\n return;\n }\n\n localStorage.setItem(cacheKey, provider);\n localStorage.setItem(`${cacheKey}:benchmarks`, JSON.stringify(benchmarks));\n}\n\nfunction createProbeCanvas():\n | HTMLCanvasElement\n | OffscreenCanvas\n | null {\n if (typeof OffscreenCanvas !== \"undefined\") {\n return new OffscreenCanvas(1, 1);\n }\n\n if (typeof document !== \"undefined\") {\n return document.createElement(\"canvas\");\n }\n\n return null;\n}\n","import type { OcrBox } from \"../types\";\nimport { clamp } from \"../utils/math\";\n\nexport interface DetectionPostprocessOptions {\n sourceWidth: number;\n sourceHeight: number;\n resizedWidth: number;\n resizedHeight: number;\n threshold: number;\n boxThreshold: number;\n unclipRatio: number;\n minSize: number;\n}\n\ninterface ComponentBox {\n minX: number;\n minY: number;\n maxX: number;\n maxY: number;\n pixels: number;\n scoreSum: number;\n}\n\nfunction createAxisAlignedBox(\n component: ComponentBox,\n options: DetectionPostprocessOptions\n): OcrBox | null {\n const width = component.maxX - component.minX + 1;\n const height = component.maxY - component.minY + 1;\n const score = component.scoreSum / component.pixels;\n\n if (width < options.minSize || height < options.minSize || score < options.boxThreshold) {\n return null;\n }\n\n const distance =\n ((width * height) * options.unclipRatio) / Math.max(2 * (width + height), 1);\n const left = clamp(component.minX - distance, 0, options.resizedWidth - 1);\n const top = clamp(component.minY - distance, 0, options.resizedHeight - 1);\n const right = clamp(component.maxX + distance, 0, options.resizedWidth - 1);\n const bottom = clamp(component.maxY + distance, 0, options.resizedHeight - 1);\n const scaleX = options.sourceWidth / options.resizedWidth;\n const scaleY = options.sourceHeight / options.resizedHeight;\n\n return {\n score,\n points: [\n { x: left * scaleX, y: top * scaleY },\n { x: right * scaleX, y: top * scaleY },\n { x: right * scaleX, y: bottom * scaleY },\n { x: left * scaleX, y: bottom * scaleY }\n ]\n };\n}\n\nfunction normalizeProbabilityMap(\n raw: Float32Array,\n dims: readonly number[]\n): { map: Float32Array; height: number; width: number } {\n if (dims.length === 4) {\n return {\n map: raw,\n height: dims[2] ?? 0,\n width: dims[3] ?? 0\n };\n }\n\n if (dims.length === 3) {\n return {\n map: raw,\n height: dims[1] ?? 0,\n width: dims[2] ?? 0\n };\n }\n\n throw new Error(`Unsupported detection output shape: ${dims.join(\"x\")}`);\n}\n\nexport function postprocessDetection(\n raw: Float32Array,\n dims: readonly number[],\n options: DetectionPostprocessOptions\n): OcrBox[] {\n const { map, height, width } = normalizeProbabilityMap(raw, dims);\n const visited = new Uint8Array(width * height);\n const queue = new Uint32Array(width * height);\n const results: OcrBox[] = [];\n\n for (let y = 0; y < height; y += 1) {\n for (let x = 0; x < width; x += 1) {\n const root = y * width + x;\n if (visited[root] || map[root] < options.threshold) {\n continue;\n }\n\n let head = 0;\n let tail = 0;\n let component: ComponentBox = {\n minX: x,\n minY: y,\n maxX: x,\n maxY: y,\n pixels: 0,\n scoreSum: 0\n };\n\n visited[root] = 1;\n queue[tail] = root;\n tail += 1;\n\n while (head < tail) {\n const current = queue[head];\n head += 1;\n const cx = current % width;\n const cy = Math.floor(current / width);\n\n component.minX = Math.min(component.minX, cx);\n component.minY = Math.min(component.minY, cy);\n component.maxX = Math.max(component.maxX, cx);\n component.maxY = Math.max(component.maxY, cy);\n component.pixels += 1;\n component.scoreSum += map[current];\n\n for (let ny = cy - 1; ny <= cy + 1; ny += 1) {\n if (ny < 0 || ny >= height) {\n continue;\n }\n\n for (let nx = cx - 1; nx <= cx + 1; nx += 1) {\n if (nx < 0 || nx >= width) {\n continue;\n }\n\n const next = ny * width + nx;\n if (visited[next] || map[next] < options.threshold) {\n continue;\n }\n\n visited[next] = 1;\n queue[tail] = next;\n tail += 1;\n }\n }\n }\n\n const box = createAxisAlignedBox(component, options);\n if (box) {\n results.push(box);\n }\n }\n }\n\n return results;\n}\n","import { resizeImageData } from \"../io/canvas\";\nimport { roundUpToMultiple } from \"../utils/math\";\n\n// PaddleOCR normalizes in BGR channel order with ImageNet values\nconst DET_MEAN_BGR = [0.485, 0.456, 0.406] as const;\nconst DET_STD_BGR = [0.229, 0.224, 0.225] as const;\n\nexport interface DetectionPreprocessResult {\n data: Float32Array;\n dims: [1, 3, number, number];\n resizedWidth: number;\n resizedHeight: number;\n}\n\nexport interface RecognitionPreprocessResult {\n data: Float32Array;\n dims: [1, number, number, number];\n}\n\nexport function preprocessDetection(\n source: ImageData,\n limitSideLen: number\n): DetectionPreprocessResult {\n const maxSide = Math.max(source.width, source.height);\n const scale = maxSide > limitSideLen ? limitSideLen / maxSide : 1;\n const resizedWidth = roundUpToMultiple(Math.max(32, Math.round(source.width * scale)), 32);\n const resizedHeight = roundUpToMultiple(Math.max(32, Math.round(source.height * scale)), 32);\n const resized = resizeImageData(source, resizedWidth, resizedHeight);\n const tensor = new Float32Array(3 * resizedWidth * resizedHeight);\n\n for (let y = 0; y < resizedHeight; y += 1) {\n for (let x = 0; x < resizedWidth; x += 1) {\n const pixelOffset = (y * resizedWidth + x) * 4;\n const red = resized.data[pixelOffset] / 255;\n const green = resized.data[pixelOffset + 1] / 255;\n const blue = resized.data[pixelOffset + 2] / 255;\n const spatialOffset = y * resizedWidth + x;\n tensor[spatialOffset] = (blue - DET_MEAN_BGR[0]) / DET_STD_BGR[0];\n tensor[resizedWidth * resizedHeight + spatialOffset] =\n (green - DET_MEAN_BGR[1]) / DET_STD_BGR[1];\n tensor[2 * resizedWidth * resizedHeight + spatialOffset] =\n (red - DET_MEAN_BGR[2]) / DET_STD_BGR[2];\n }\n }\n\n return {\n data: tensor,\n dims: [1, 3, resizedHeight, resizedWidth],\n resizedWidth,\n resizedHeight\n };\n}\n\nexport function preprocessRecognition(\n source: ImageData,\n imageShape: readonly [number, number, number]\n): RecognitionPreprocessResult {\n const [, targetHeight, targetWidth] = imageShape;\n const ratio = source.width / Math.max(source.height, 1);\n const resizedWidth = Math.min(targetWidth, Math.max(1, Math.round(targetHeight * ratio)));\n const resized = resizeImageData(source, resizedWidth, targetHeight);\n const tensor = new Float32Array(3 * targetHeight * targetWidth);\n\n for (let y = 0; y < targetHeight; y += 1) {\n for (let x = 0; x < targetWidth; x += 1) {\n const spatialOffset = y * targetWidth + x;\n const channelOffset = spatialOffset;\n\n if (x < resizedWidth) {\n const pixelOffset = (y * resizedWidth + x) * 4;\n tensor[channelOffset] = resized.data[pixelOffset + 2] / 127.5 - 1;\n tensor[targetHeight * targetWidth + spatialOffset] =\n resized.data[pixelOffset + 1] / 127.5 - 1;\n tensor[2 * targetHeight * targetWidth + spatialOffset] =\n resized.data[pixelOffset] / 127.5 - 1;\n } else {\n tensor[channelOffset] = 0;\n tensor[targetHeight * targetWidth + spatialOffset] = 0;\n tensor[2 * targetHeight * targetWidth + spatialOffset] = 0;\n }\n }\n }\n\n return {\n data: tensor,\n dims: [1, 3, targetHeight, targetWidth]\n };\n}\n","import type { OcrLine } from \"../types\";\nimport { maxIndex, maxProbability } from \"../utils/math\";\n\ninterface DecodeOptions {\n blankIndex?: number;\n}\n\nexport function decodeRecognitionOutput(\n output: Float32Array,\n dims: readonly number[],\n dictionary: readonly string[],\n options: DecodeOptions = {}\n): Array> {\n if (dims.length !== 3) {\n throw new Error(`Unsupported recognition output shape: ${dims.join(\"x\")}`);\n }\n\n const [batch, second, third] = dims;\n const blankIndex = options.blankIndex ?? 0;\n const isBatchTimeClass = second < third;\n const timeSteps = isBatchTimeClass ? second : third;\n const classes = isBatchTimeClass ? third : second;\n const results: Array> = [];\n\n for (let batchIndex = 0; batchIndex < batch; batchIndex += 1) {\n let lastIndex = -1;\n const pieces: string[] = [];\n const confidences: number[] = [];\n\n for (let step = 0; step < timeSteps; step += 1) {\n const logits = new Float32Array(classes);\n\n for (let classIndex = 0; classIndex < classes; classIndex += 1) {\n const offset = isBatchTimeClass\n ? (batchIndex * timeSteps + step) * classes + classIndex\n : (batchIndex * classes + classIndex) * timeSteps + step;\n logits[classIndex] = output[offset];\n }\n\n const prediction = maxIndex(logits);\n if (prediction.index === blankIndex || prediction.index === lastIndex) {\n lastIndex = prediction.index;\n continue;\n }\n\n const token = dictionary[prediction.index - 1];\n if (token) {\n pieces.push(token);\n confidences.push(maxProbability(logits));\n }\n\n lastIndex = prediction.index;\n }\n\n const score =\n confidences.length === 0\n ? 0\n : confidences.reduce((total, value) => total + value, 0) / confidences.length;\n\n results.push({\n text: pieces.join(\"\"),\n score\n });\n }\n\n return results;\n}\n","import type {\n CreatePaddleOcrOptions,\n DictionarySource,\n ExecutionProvider,\n OcrBox,\n OcrImageSource,\n OcrLine,\n OcrOptions,\n OcrProgressCallback,\n OcrResult,\n RuntimeSelection\n} from \"./types\";\nimport { cropImageData, ensureImageData } from \"./io/canvas\";\nimport { createSession, configureOrt, runSession, type OrtSession } from \"./runtime/ort\";\nimport { getProviderCandidates } from \"./runtime/provider\";\nimport { postprocessDetection } from \"./pipeline/detect\";\nimport { preprocessDetection, preprocessRecognition } from \"./pipeline/preprocess\";\nimport { decodeRecognitionOutput } from \"./pipeline/recognize\";\nimport { axisAlignedBounds, sortBoxes } from \"./utils/math\";\n\nconst DEFAULT_RECOGNITION_SHAPE = [3, 48, 320] as const;\n\nfunction now(): number {\n return typeof performance !== \"undefined\" ? performance.now() : Date.now();\n}\n\nfunction isDictionaryUrlSource(source: DictionarySource): source is { url: string } {\n return !Array.isArray(source);\n}\n\nasync function loadDictionary(source: DictionarySource): Promise {\n if (!isDictionaryUrlSource(source)) {\n return [...source];\n }\n\n const response = await fetch(source.url);\n if (!response.ok) {\n throw new Error(`Unable to fetch dictionary from ${source.url}`);\n }\n\n const text = await response.text();\n const lines = text.split(/\\r?\\n/u);\n if (lines.at(-1) === \"\") {\n lines.pop();\n }\n return lines;\n}\n\nfunction createBlankTensor(width: number, height: number): Float32Array {\n return new Float32Array(3 * width * height);\n}\n\nfunction combineRecognitionBatch(\n batch: readonly Float32Array[],\n shape: readonly [number, number, number]\n): Float32Array {\n const [channels, height, width] = shape;\n const itemSize = channels * height * width;\n const merged = new Float32Array(batch.length * itemSize);\n\n batch.forEach((tensor, index) => {\n merged.set(tensor, index * itemSize);\n });\n\n return merged;\n}\n\nexport class PaddleOcrWeb {\n private readonly options: CreatePaddleOcrOptions;\n\n private detectorSession: OrtSession | null = null;\n\n private recognizerSession: OrtSession | null = null;\n\n private dictionary: string[] = [];\n\n private runtimeSelection: RuntimeSelection | null = null;\n\n private initPromise: Promise | null = null;\n\n constructor(options: CreatePaddleOcrOptions) {\n this.options = options;\n }\n\n async init(onProgress?: OcrProgressCallback): Promise {\n if (!this.initPromise) {\n this.initPromise = this.doInit(onProgress);\n }\n\n return this.initPromise;\n }\n\n private async doInit(onProgress?: OcrProgressCallback): Promise {\n configureOrt(this.options.ort);\n\n onProgress?.({ phase: \"loading_dictionary\" });\n this.dictionary = await loadDictionary(this.options.manifest.dictionary);\n\n const provider = await this.selectProvider();\n\n const useCache = this.options.cacheModels;\n\n onProgress?.({ phase: \"loading_detection_model\" });\n this.detectorSession = await createSession(\n this.options.manifest.detection.url,\n provider,\n onProgress\n ? (loaded, totalBytes) =>\n onProgress({ phase: \"loading_detection_model\", loaded, totalBytes: totalBytes ?? undefined })\n : undefined,\n useCache\n );\n\n onProgress?.({ phase: \"loading_recognition_model\" });\n this.recognizerSession = await createSession(\n this.options.manifest.recognition.url,\n provider,\n onProgress\n ? (loaded, totalBytes) =>\n onProgress({ phase: \"loading_recognition_model\", loaded, totalBytes: totalBytes ?? undefined })\n : undefined,\n useCache\n );\n\n if (this.options.warmup) {\n onProgress?.({ phase: \"warmup\" });\n await this.warmupSessions();\n }\n }\n\n private async selectProvider(): Promise {\n const candidates = getProviderCandidates(this.options.providerPreference);\n const firstCandidate = candidates[0];\n if (!firstCandidate) {\n throw new Error(\"No ONNX Runtime execution providers are available.\");\n }\n\n this.runtimeSelection = {\n provider: firstCandidate,\n candidates,\n benchmarked: false,\n benchmarks: []\n };\n\n return firstCandidate;\n }\n\n async warmup(): Promise {\n await this.init();\n await this.warmupSessions();\n }\n\n private async warmupSessions(): Promise {\n if (!this.detectorSession || !this.recognizerSession) {\n return;\n }\n\n await runSession(\n this.detectorSession,\n createBlankTensor(640, 640),\n [1, 3, 640, 640],\n this.options.manifest.detection.inputName,\n this.options.manifest.detection.outputName\n );\n\n const recognitionShape =\n this.options.manifest.recognitionImageShape ?? DEFAULT_RECOGNITION_SHAPE;\n const [channels, height, width] = recognitionShape;\n await runSession(\n this.recognizerSession,\n new Float32Array(channels * height * width),\n [1, channels, height, width],\n this.options.manifest.recognition.inputName,\n this.options.manifest.recognition.outputName\n );\n }\n\n getRuntimeSelection(): RuntimeSelection | null {\n return this.runtimeSelection;\n }\n\n async ocr(source: OcrImageSource, options: OcrOptions = {}): Promise {\n const onProgress = options.onProgress;\n await this.init(onProgress);\n\n if (!this.detectorSession || !this.recognizerSession || !this.runtimeSelection) {\n throw new Error(\"The OCR runtime was not initialized.\");\n }\n\n onProgress?.({ phase: \"preprocessing\" });\n const image = await ensureImageData(source);\n const detectionInput = preprocessDetection(\n image,\n this.options.manifest.detectionLimitSideLen ?? 960\n );\n\n onProgress?.({ phase: \"detecting\" });\n const detectionOutput = await runSession(\n this.detectorSession,\n detectionInput.data,\n detectionInput.dims,\n this.options.manifest.detection.inputName,\n this.options.manifest.detection.outputName\n );\n\n const rawDetection = detectionOutput.data as Float32Array;\n const boxes = sortBoxes(\n postprocessDetection(rawDetection, detectionOutput.dims, {\n sourceWidth: image.width,\n sourceHeight: image.height,\n resizedWidth: detectionInput.resizedWidth,\n resizedHeight: detectionInput.resizedHeight,\n threshold: this.options.manifest.detectionThreshold ?? 0.3,\n boxThreshold: this.options.manifest.detectionBoxThreshold ?? 0.6,\n unclipRatio: this.options.manifest.detectionUnclipRatio ?? 1.5,\n minSize: this.options.manifest.detectionMinSize ?? 3\n })\n );\n const lines = await this.recognizeBoxes(image, boxes, options.maxRecognitionBatchSize ?? 8, onProgress);\n\n return {\n text: lines.map((line) => line.text).join(\"\\n\"),\n lines,\n image: {\n width: image.width,\n height: image.height\n },\n runtime: this.runtimeSelection\n };\n }\n\n private async recognizeBoxes(\n image: ImageData,\n boxes: readonly OcrBox[],\n batchSize: number,\n onProgress?: OcrProgressCallback\n ): Promise {\n if (!this.recognizerSession) {\n throw new Error(\"Recognizer session is not available.\");\n }\n\n const recognitionShape =\n this.options.manifest.recognitionImageShape ?? DEFAULT_RECOGNITION_SHAPE;\n const lines: OcrLine[] = [];\n const totalBatches = Math.ceil(boxes.length / batchSize);\n let batchIndex = 0;\n\n for (let cursor = 0; cursor < boxes.length; cursor += batchSize) {\n onProgress?.({ phase: \"recognizing\", current: batchIndex + 1, total: totalBatches });\n const batchBoxes = boxes.slice(cursor, cursor + batchSize);\n const tensors: Float32Array[] = [];\n\n for (const box of batchBoxes) {\n const bounds = axisAlignedBounds(box);\n const crop = cropImageData(image, bounds.left, bounds.top, bounds.width, bounds.height);\n tensors.push(preprocessRecognition(crop, recognitionShape).data);\n }\n\n const merged = combineRecognitionBatch(tensors, recognitionShape);\n const [channels, height, width] = recognitionShape;\n const recognitionOutput = await runSession(\n this.recognizerSession,\n merged,\n [batchBoxes.length, channels, height, width],\n this.options.manifest.recognition.inputName,\n this.options.manifest.recognition.outputName\n );\n const decoded = decodeRecognitionOutput(\n recognitionOutput.data as Float32Array,\n recognitionOutput.dims,\n this.dictionary\n );\n\n decoded.forEach((item, index) => {\n const box = batchBoxes[index];\n lines.push({\n text: item.text,\n score: (item.score + box.score) / 2,\n box\n });\n });\n\n batchIndex++;\n }\n\n return lines.filter((line) => line.text.length > 0);\n }\n\n dispose(): void {\n this.detectorSession = null;\n this.recognizerSession = null;\n }\n}\n\nexport function createPaddleOcr(options: CreatePaddleOcrOptions): PaddleOcrWeb {\n return new PaddleOcrWeb(options);\n}\n","import type { PaddleOcrModelManifest, PPOcrV5ModelVariant } from \"../types\";\n\nexport interface OfficialManifestOptions {\n baseUrl: string;\n dictionaryUrl?: string;\n modelVariant?: PPOcrV5ModelVariant;\n detectionModelPath?: string;\n recognitionModelPath?: string;\n}\n\nexport const DEFAULT_DICTIONARY_URL =\n \"https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/main/ppocr/utils/dict/ppocrv5_dict.txt\";\n\nexport const PPOCRV5_MODEL_PATHS = {\n detection: {\n server: \"det_server.onnx\",\n mobile: \"det_mobile.onnx\"\n },\n recognition: {\n server: \"rec_server.onnx\",\n mobile: \"rec_mobile.onnx\"\n }\n} as const;\n\nfunction resolveDetectionPath(options: OfficialManifestOptions): string {\n if (options.detectionModelPath) {\n return options.detectionModelPath;\n }\n return PPOCRV5_MODEL_PATHS.detection[options.modelVariant ?? \"mobile\"];\n}\n\nfunction resolveRecognitionPath(options: OfficialManifestOptions): string {\n if (options.recognitionModelPath) {\n return options.recognitionModelPath;\n }\n return PPOCRV5_MODEL_PATHS.recognition[options.modelVariant ?? \"mobile\"];\n}\n\nexport function createPPOcrV5BrowserManifest(\n options: OfficialManifestOptions\n): PaddleOcrModelManifest {\n const normalizedBase = options.baseUrl.replace(/\\/$/, \"\");\n const variant = options.modelVariant ?? \"mobile\";\n\n return {\n id: variant === \"server\" ? \"pp-ocrv5-browser\" : `pp-ocrv5-${variant}-browser`,\n detection: {\n url: `${normalizedBase}/${resolveDetectionPath(options)}`\n },\n recognition: {\n url: `${normalizedBase}/${resolveRecognitionPath(options)}`\n },\n dictionary: {\n url: options.dictionaryUrl ?? DEFAULT_DICTIONARY_URL\n },\n detectionLimitSideLen: 960,\n detectionThreshold: 0.3,\n detectionBoxThreshold: 0.6,\n detectionUnclipRatio: 1.5,\n detectionMinSize: 3,\n recognitionImageShape: [3, 48, 320]\n };\n}\n","import { createPPOcrV5BrowserManifest } from \"../manifests/official\";\nimport { createPaddleOcr, type PaddleOcrWeb } from \"../paddle-ocr\";\nimport type {\n CreatePPOcrV5Options,\n OcrImageSource,\n OcrOptions,\n OcrResult,\n OrtRuntimeOptions\n} from \"../types\";\n\nexport const DEFAULT_ORT_WASM_PATHS = \"https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/\";\nexport const DEFAULT_MODEL_RELEASE_TAG = \"models-ppocrv5-v4\";\nexport const DEFAULT_MODEL_BASE_URL =\n \"https://zxc88645.github.io/ffocr/models/pp-ocrv5\";\n\nfunction createRecommendedOrtOptions(options?: OrtRuntimeOptions): OrtRuntimeOptions {\n return {\n wasmPaths: options?.wasmPaths ?? DEFAULT_ORT_WASM_PATHS,\n wasmThreads: options?.wasmThreads\n };\n}\n\nexport function createPPOcrV5(options: CreatePPOcrV5Options): PaddleOcrWeb {\n const {\n baseUrl,\n dictionaryUrl,\n modelVariant,\n detectionModelPath,\n recognitionModelPath,\n ort,\n ...rest\n } = options;\n\n return createPaddleOcr({\n ...rest,\n manifest: createPPOcrV5BrowserManifest({\n baseUrl,\n dictionaryUrl,\n modelVariant,\n detectionModelPath,\n recognitionModelPath\n }),\n ort: createRecommendedOrtOptions(ort)\n });\n}\n\nexport function createDefaultPPOcrV5(\n options: Omit = {}\n): PaddleOcrWeb {\n return createPPOcrV5({\n ...options,\n baseUrl: DEFAULT_MODEL_BASE_URL\n });\n}\n\nexport async function ocrWithPPOcrV5(\n source: OcrImageSource,\n options: CreatePPOcrV5Options,\n ocrOptions?: OcrOptions\n): Promise {\n const runtime = createPPOcrV5(options);\n\n try {\n return await runtime.ocr(source, ocrOptions);\n } finally {\n runtime.dispose();\n }\n}\n\nexport async function ocrWithDefaultPPOcrV5(\n source: OcrImageSource,\n options: Omit = {},\n ocrOptions?: OcrOptions\n): Promise {\n return ocrWithPPOcrV5(\n source,\n {\n ...options,\n baseUrl: DEFAULT_MODEL_BASE_URL\n },\n ocrOptions\n 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