{"version":3,"file":"index.cjs","names":[],"sources":["../src/constants.ts","../src/utils/image.ts","../src/processor/detection.ts","../src/processor/recognition.ts","../src/processor/paddle-ocr.ts"],"sourcesContent":["import type {\n DetectionRuntimeOptions,\n PaddleOptions,\n ProcessRecognitionOptions,\n RecognitionOrderingOptions,\n RecognitionRuntimeOptions,\n} from \"./interface.ts\";\n\ntype DetectionDefaults = Required;\ntype RecognitionDefaults = Required;\ntype RecognitionOrderingDefaults = Required;\ntype ProcessRecognitionDefaults = Required;\n\nexport const DEFAULT_DETECTION_OPTIONS: DetectionDefaults = {\n padding: 0,\n mean: [0.485 * 255, 0.456 * 255, 0.406 * 255],\n stdDeviation: [1 / 0.229 / 255, 1 / 0.224 / 255, 1 / 0.255 / 255],\n maxSideLength: 960,\n textPixelThreshold: 0.5,\n minimumAreaThreshold: 20,\n paddingBoxVertical: 0.4,\n paddingBoxHorizontal: 0.6,\n dilationKernelSize: 1,\n};\n\nexport const DEFAULT_RECOGNITION_OPTIONS: RecognitionDefaults = {\n mean: [127.5, 127.5, 127.5],\n stdDeviation: [1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5],\n imageHeight: 48,\n charactersDictionary: [],\n};\n\nexport const DEFAULT_RECOGNITION_ORDERING_OPTIONS: RecognitionOrderingDefaults = {\n sortByReadingOrder: true,\n sameLineThresholdRatio: 0.25,\n};\n\nexport const DEFAULT_PROCESS_RECOGNITION_OPTIONS: ProcessRecognitionDefaults = {\n lineMergeThresholdRatio: 0.5,\n};\n\nexport const DEFAULT_PADDLE_OPTIONS: Partial = {\n detection: DEFAULT_DETECTION_OPTIONS,\n recognition: DEFAULT_RECOGNITION_OPTIONS,\n};\n","import type { Box } from \"../interface.ts\";\n\ninterface CropOptions {\n x: number;\n y: number;\n width: number;\n height: number;\n}\n\ninterface ResizeOptions {\n width?: number;\n height?: number;\n filter?: \"triangle\";\n}\n\ninterface PaddingOptions {\n padding?: number;\n vertical?: number;\n horizontal?: number;\n top?: number;\n bottom?: number;\n left?: number;\n right?: number;\n color?: number[];\n}\n\ninterface TensorOptions {\n mean_values: [number, number, number];\n norm_values: [number, number, number];\n}\n\ninterface DilateOptions {\n norm?: \"LInf\";\n k?: number;\n}\n\ninterface ThresholdOptions {\n threshold?: number; // 阈值，默认128\n}\n\ninterface ContoursOptions {\n minArea?: number;\n}\n\ninterface RectOptions {\n x: number;\n y: number;\n width: number;\n height: number;\n lineWidth?: number;\n color?: number[];\n}\n\nexport class Image {\n width: number;\n height: number;\n data: Uint8Array;\n depth: 8;\n channels: number;\n\n /**\n * 创建一个新的 Image 实例。\n * @param width 图像的宽度\n * @param height 图像的高度\n * @param data 图像数据，Uint8Array\n */\n constructor(width: number, height: number, channels: number, data: Uint8Array) {\n this.width = width;\n this.height = height;\n this.channels = channels;\n this.depth = 8;\n if (data) {\n this.data = data;\n } else {\n const length = width * height * 4;\n this.data = new Uint8Array(length); // Default to RGBA\n }\n }\n\n /**\n * 裁剪\n */\n crop(options: CropOptions) {\n const { x, y, width, height } = options;\n if (x < 0 || y < 0 || x + width > this.width || y + height > this.height) {\n throw new Error(\"Crop area is out of bounds\");\n }\n const croppedData = new Uint8Array(width * height * this.channels);\n for (let j = 0; j < height; j++) {\n for (let i = 0; i < width; i++) {\n const srcIndex = ((y + j) * this.width + (x + i)) * this.channels;\n const dstIndex = (j * width + i) * this.channels;\n croppedData.set(this.data.subarray(srcIndex, srcIndex + this.channels), dstIndex);\n }\n }\n return new Image(width, height, this.channels, croppedData);\n }\n\n /**\n * 将图片缩放到指定的尺寸w\n * @param options\n */\n resize(options: ResizeOptions) {\n let { width, height } = options;\n if (width === undefined && height === undefined) {\n throw new Error(\"At least one of width or height must be specified\");\n }\n if (width === undefined) {\n width = Math.round(this.width * ((height ?? this.height) / this.height));\n }\n if (height === undefined) {\n height = Math.round(this.height * (width / this.width));\n }\n\n const srcW = this.width;\n const srcH = this.height;\n const dstW = width;\n const dstH = height;\n const channels = this.channels;\n const srcData = this.data;\n\n function triangle_kernel(x: number) {\n x = Math.abs(x);\n return x < 1 ? 1 - x : 0;\n }\n\n function clamp(v: number, min: number, max: number): number {\n return Math.max(min, Math.min(max, v));\n }\n\n // 1. 纵向采样\n const tmpData = new Float32Array(srcW * dstH * channels);\n const ratioY = srcH / dstH;\n const sratioY = ratioY < 1 ? 1 : ratioY;\n const supportY = 1.0 * sratioY;\n\n for (let outy = 0; outy < dstH; outy++) {\n const inputy = (outy + 0.5) * ratioY - 0.5;\n const left = Math.max(0, Math.floor(inputy - supportY));\n const right = Math.min(srcH, Math.ceil(inputy + supportY));\n\n const ws: number[] = [];\n let sum = 0;\n for (let i = left; i < right; i++) {\n const w = triangle_kernel((i - inputy) / sratioY);\n ws.push(w);\n sum += w;\n }\n for (let i = 0; i < ws.length; i++) ws[i] /= sum;\n\n for (let x = 0; x < srcW; x++) {\n for (let c = 0; c < channels; c++) {\n let t = 0;\n for (let i = 0; i < ws.length; i++) {\n const srcIdx = ((left + i) * srcW + x) * channels + c;\n t += srcData[srcIdx] * ws[i];\n }\n tmpData[(outy * srcW + x) * channels + c] = t;\n }\n }\n }\n\n // 2. 横向采样\n const dstData = new Uint8Array(dstW * dstH * channels);\n const ratioX = srcW / dstW;\n const sratioX = ratioX < 1 ? 1 : ratioX;\n const supportX = 1.0 * sratioX;\n\n for (let outx = 0; outx < dstW; outx++) {\n const inputx = (outx + 0.5) * ratioX - 0.5;\n const left = Math.max(0, Math.floor(inputx - supportX));\n const right = Math.min(srcW, Math.ceil(inputx + supportX));\n\n const ws: number[] = [];\n let sum = 0;\n for (let i = left; i < right; i++) {\n const w = triangle_kernel((i - inputx) / sratioX);\n ws.push(w);\n sum += w;\n }\n for (let i = 0; i < ws.length; i++) ws[i] /= sum;\n\n for (let y = 0; y < dstH; y++) {\n for (let c = 0; c < channels; c++) {\n let t = 0;\n for (let i = 0; i < ws.length; i++) {\n const srcIdx = (y * srcW + (left + i)) * channels + c;\n t += tmpData[srcIdx] * ws[i];\n }\n dstData[(y * dstW + outx) * channels + c] = Math.round(clamp(t, 0, 255));\n }\n }\n }\n\n return new Image(dstW, dstH, channels, dstData);\n }\n\n /**\n * 为图片添加指定颜色的边距，默认为透明的\n * @param options\n */\n padding(options: PaddingOptions) {\n // 解析边距\n let { padding, vertical, horizontal, top, bottom, left, right, color } = options;\n // 优先级：padding > vertical/horizontal > top/bottom/left/right\n if (typeof padding === \"number\") {\n top = bottom = left = right = padding;\n } else {\n if (typeof vertical === \"number\") {\n top = bottom = vertical;\n }\n if (typeof horizontal === \"number\") {\n left = right = horizontal;\n }\n }\n top = top ?? 0;\n bottom = bottom ?? 0;\n left = left ?? 0;\n right = right ?? 0;\n color = color ?? [0, 0, 0, 0];\n const newW = this.width + left + right;\n const newH = this.height + top + bottom;\n const newData = new Uint8Array(newW * newH * 4);\n // 填充背景色\n for (let y = 0; y < newH; y++) {\n for (let x = 0; x < newW; x++) {\n const idx = (y * newW + x) * 4;\n newData[idx] = color[0];\n newData[idx + 1] = color[1];\n newData[idx + 2] = color[2];\n newData[idx + 3] = color[3];\n }\n }\n // 拷贝原图\n for (let y = 0; y < this.height; y++) {\n for (let x = 0; x < this.width; x++) {\n const srcIdx = (y * this.width + x) * 4;\n const dstIdx = ((y + top) * newW + (x + left)) * 4;\n newData.set(this.data.subarray(srcIdx, srcIdx + 4), dstIdx);\n }\n }\n return new Image(newW, newH, this.channels, newData);\n }\n\n /**\n * 将当前图像转换为张量格式，以便输入到onnx模型\n * @param options\n */\n tensor(options: TensorOptions): Float32Array {\n const mean = options.mean_values;\n const norm = options.norm_values;\n const width = this.width;\n const height = this.height;\n const numChannels = 3;\n const rgbaData = this.data;\n const tensor = new Float32Array(width * height * numChannels);\n for (let h = 0; h < height; h++) {\n for (let w = 0; w < width; w++) {\n const pixelIndex = (h * width + w) * this.channels;\n const tensorIndex = h * width + w;\n for (let c = 0; c < numChannels; c++) {\n const pixelValue = rgbaData[pixelIndex + c];\n const normalizedValue = pixelValue * norm[c] - mean[c] * norm[c];\n tensor[c * height * width + tensorIndex] = normalizedValue;\n }\n }\n }\n return tensor;\n }\n\n /**\n * 灰度图阈值方法，大于阈值的像素点设为255，小于等于阈值的设为0\n * @param options\n */\n threshold(options: ThresholdOptions) {\n const threshold = options.threshold ?? 128;\n const width = this.width;\n const height = this.height;\n // 创建二值化图像\n const binData = new Uint8Array(width * height);\n for (let i = 0; i < width * height; i++) {\n binData[i] = this.data[i * this.channels] > threshold ? 255 : 0;\n }\n return new Image(width, height, 1, binData);\n }\n\n /**\n * 膨胀操作，使用指定的范数和核大小\n * 进行处理的图片像素是0和255，膨胀255的像素点\n * 返回一个新的图片\n * @param options\n */\n dilate(options: DilateOptions = {}): Image {\n const { norm = \"LInf\", k = 1 } = options;\n if (norm !== \"LInf\") {\n throw new Error(\"Only LInf norm is supported\");\n }\n if (this.channels !== 1) {\n throw new Error(\"Dilate only supports single channel (grayscale) images\");\n }\n const width = this.width;\n const height = this.height;\n const src = this.data;\n // 1. 计算每个像素到最近前景像素(255)的LInf距离\n // 初始化距离图，前景为0，背景为无穷大\n const INF = 999999;\n const dist = new Uint16Array(width * height);\n for (let i = 0; i < width * height; i++) {\n dist[i] = src[i] > 0 ? 0 : INF;\n }\n // 两次扫描，先左上到右下，再右下到左上\n // LInf: 只需看8邻域的最小距离+1\n // 正向\n for (let y = 0; y < height; y++) {\n for (let x = 0; x < width; x++) {\n const idx = y * width + x;\n if (dist[idx] === 0) continue;\n let minDist = INF;\n for (let dy = -1; dy <= 0; dy++) {\n for (let dx = -1; dx <= 1; dx++) {\n if (dx === 0 && dy === 0) continue;\n const nx = x + dx,\n ny = y + dy;\n if (nx >= 0 && nx < width && ny >= 0 && ny < height) {\n const nidx = ny * width + nx;\n minDist = Math.min(minDist, dist[nidx] + 1);\n }\n }\n }\n dist[idx] = Math.min(dist[idx], minDist);\n }\n }\n // 反向\n for (let y = height - 1; y >= 0; y--) {\n for (let x = width - 1; x >= 0; x--) {\n const idx = y * width + x;\n if (dist[idx] === 0) continue;\n let minDist = INF;\n for (let dy = 0; dy <= 1; dy++) {\n for (let dx = -1; dx <= 1; dx++) {\n if (dx === 0 && dy === 0) continue;\n const nx = x + dx,\n ny = y + dy;\n if (nx >= 0 && nx < width && ny >= 0 && ny < height) {\n const nidx = ny * width + nx;\n minDist = Math.min(minDist, dist[nidx] + 1);\n }\n }\n }\n dist[idx] = Math.min(dist[idx], minDist);\n }\n }\n // 2. 距离小于等于k的像素设为255，否则为0\n const out = new Uint8Array(width * height);\n for (let i = 0; i < width * height; i++) {\n out[i] = dist[i] <= k ? 255 : 0;\n }\n return new Image(width, height, 1, out);\n }\n\n /**\n * 获取图像中的轮廓\n * @returns\n */\n contours(options: ContoursOptions = {}): Box[] {\n // Suzuki/Abe 边界跟踪算法，输入为灰度图，输出 Box[]\n const minArea = options.minArea ?? 1;\n const width = this.width;\n const height = this.height;\n // 二值化\n const bin = new Uint8Array(width * height);\n for (let i = 0; i < width * height; i++) {\n bin[i] = this.data[i] > 0 ? 1 : 0;\n }\n // 轮廓提取\n const visited = new Uint8Array(width * height);\n const boxes: Box[] = [];\n const at = (x: number, y: number) => y * width + x;\n for (let y = 0; y < height; y++) {\n for (let x = 0; x < width; x++) {\n if (bin[at(x, y)] && !visited[at(x, y)]) {\n // BFS 寻找连通域\n let minX = x,\n minY = y,\n maxX = x,\n maxY = y,\n area = 0;\n const queue = [[x, y]];\n visited[at(x, y)] = 1;\n while (queue.length) {\n const current = queue.shift();\n if (!current) {\n break;\n }\n const [cx, cy] = current;\n area++;\n minX = Math.min(minX, cx);\n minY = Math.min(minY, cy);\n maxX = Math.max(maxX, cx);\n maxY = Math.max(maxY, cy);\n for (const [dx, dy] of [\n [-1, 0],\n [1, 0],\n [0, -1],\n [0, 1],\n [-1, -1],\n [1, -1],\n [-1, 1],\n [1, 1],\n ]) {\n const nx = cx + dx,\n ny = cy + dy;\n if (\n nx >= 0 &&\n nx < width &&\n ny >= 0 &&\n ny < height &&\n bin[at(nx, ny)] &&\n !visited[at(nx, ny)]\n ) {\n visited[at(nx, ny)] = 1;\n queue.push([nx, ny]);\n }\n }\n }\n if (area >= minArea) {\n boxes.push({\n x: minX,\n y: minY,\n width: maxX - minX + 1,\n height: maxY - minY + 1,\n });\n }\n }\n }\n }\n return boxes;\n }\n\n /**\n * 在图像上绘制矩形，支持线宽\n * @param x 左上角x\n * @param y 左上角y\n * @param width 矩形宽度\n * @param height 矩形高度\n * @param color 颜色 [r,g,b,a]\n * @param lineWidth 线宽\n */\n rect(options: RectOptions) {\n const { x, y, width, height, color = [], lineWidth = 1 } = options;\n if (!color.length) {\n color.push(...Array(this.channels).fill(255));\n }\n if (this.channels !== color.length) {\n throw new Error(\n `Color length ${color.length} does not match image channels ${this.channels}`\n );\n }\n // 上下边\n for (let dy = 0; dy < lineWidth; dy++) {\n for (let i = 0; i < width; i++) {\n // 上边\n const yy = y + dy;\n const xx = x + i;\n if (yy >= 0 && yy < this.height && xx >= 0 && xx < this.width) {\n const idx = (yy * this.width + xx) * this.channels;\n this.data.set(color, idx);\n }\n // 下边\n const by = y + height - 1 - dy;\n if (by >= 0 && by < this.height && xx >= 0 && xx < this.width) {\n const idx = (by * this.width + xx) * this.channels;\n this.data.set(color, idx);\n }\n }\n }\n // 左右边\n for (let dx = 0; dx < lineWidth; dx++) {\n for (let j = 0; j < height; j++) {\n // 左边\n const xx = x + dx;\n const yy = y + j;\n if (xx >= 0 && xx < this.width && yy >= 0 && yy < this.height) {\n const idx = (yy * this.width + xx) * this.channels;\n this.data.set(color, idx);\n }\n // 右边\n const rx = x + width - 1 - dx;\n if (rx >= 0 && rx < this.width && yy >= 0 && yy < this.height) {\n const idx = (yy * this.width + rx) * this.channels;\n this.data.set(color, idx);\n }\n }\n }\n }\n\n /**\n * 以png格式输出到指定位置\n * @param path 输出路径\n */\n // async saveAsPng(path: string) {\n // try {\n // const { encode } = await import(\"fast-png\");\n // const pngData = encode({\n // width: this.width,\n // height: this.height,\n // depth: this.depth,\n // channels: this.channels,\n // data: this.data,\n // });\n // const { writeFile } = await import(\"fs/promises\");\n // await writeFile(path, pngData);\n // } catch (e) {\n // console.error(`Failed to save image as PNG: ${e}`);\n // throw e;\n // }\n // }\n}\n","import { DEFAULT_DETECTION_OPTIONS } from \"../constants.ts\";\nimport type {\n Box,\n DetectionRuntimeOptions,\n OcrProgress,\n OrtInferenceSession,\n OrtModule,\n PaddleOcrProgressEvent,\n} from \"../interface.ts\";\nimport { Image } from \"../utils/image.ts\";\n\nexport interface ResizeParams {\n srcWidth: number;\n srcHeight: number;\n dstWidth: number;\n dstHeight: number;\n scaleWidth: number;\n scaleHeight: number;\n}\n\n/**\n * Result of preprocessing an image for text detection\n */\nexport interface PreprocessDetectionResult {\n tensor: Float32Array;\n resizeParams: ResizeParams;\n}\n\nexport interface DetectionRunOptions extends Partial {\n onProgress?: (event: PaddleOcrProgressEvent) => void;\n}\n\n/**\n * Service for detecting text regions in images\n */\nexport class DetectionService {\n private static readonly TOTAL_PROGRESS_STEPS = 3;\n private readonly options: DetectionRuntimeOptions;\n private readonly session: OrtInferenceSession;\n private readonly ortModule: OrtModule;\n\n constructor(\n ortModule: OrtModule,\n session: OrtInferenceSession,\n options: Partial = {}\n ) {\n this.session = session;\n this.ortModule = ortModule;\n\n this.options = {\n ...DEFAULT_DETECTION_OPTIONS,\n ...options,\n };\n }\n\n /**\n * Main method to run text detection on an image\n * @param image ArrayBuffer of the image or Canvas\n */\n async run(image: Image, options: DetectionRunOptions = {}): Promise {\n const { onProgress, ...runtimeOverrides } = options;\n const runtimeOptions = this.resolveRuntimeOptions(runtimeOverrides);\n const input = await this.preprocessDetection(image, runtimeOptions);\n onProgress?.({\n type: \"det\",\n stage: \"preprocess\",\n progress: this.createProgress(1),\n });\n\n const detection = await this.runInference(input.tensor, input.resizeParams);\n onProgress?.({\n type: \"det\",\n stage: \"infer\",\n progress: this.createProgress(2),\n });\n\n const detectedBoxes = detection\n ? this.postprocessDetection(detection, input, runtimeOptions)\n : [];\n onProgress?.({\n type: \"det\",\n stage: \"postprocess\",\n progress: this.createProgress(3),\n detectedCount: detectedBoxes.length,\n });\n\n return detectedBoxes;\n }\n\n private resolveRuntimeOptions(\n options: Partial = {}\n ): DetectionRuntimeOptions {\n return {\n ...this.options,\n ...options,\n };\n }\n\n private createProgress(current: number): OcrProgress {\n return {\n current,\n remain: DetectionService.TOTAL_PROGRESS_STEPS - current,\n total: DetectionService.TOTAL_PROGRESS_STEPS,\n };\n }\n\n /**\n * Preprocess an image for text detection\n */\n private async preprocessDetection(\n image: Image,\n runtimeOptions: DetectionRuntimeOptions\n ): Promise {\n const resizeParams = this.calculateResizeDimensions(image, runtimeOptions);\n\n const resizedImage = image.resize({\n width: resizeParams.dstWidth,\n height: resizeParams.dstHeight,\n });\n const tensor = resizedImage.tensor({\n mean_values: runtimeOptions.mean,\n norm_values: runtimeOptions.stdDeviation,\n });\n\n return {\n tensor,\n resizeParams,\n };\n }\n\n /**\n * Calculate dimensions for resizing the image\n */\n private calculateResizeDimensions(image: Image, runtimeOptions: DetectionRuntimeOptions) {\n const MAX_SIDE_LEN = runtimeOptions.maxSideLength;\n const { width: srcWidth, height: srcHeight } = image;\n const ratio = srcWidth > srcHeight ? MAX_SIDE_LEN / srcWidth : MAX_SIDE_LEN / srcHeight;\n let dstWidth = Math.floor(srcWidth * ratio);\n let dstHeight = Math.floor(srcHeight * ratio);\n // Ensure dimensions are multiples of 32 for model compatibility\n if (dstWidth % 32 !== 0) dstWidth = Math.max(Math.floor(dstWidth / 32) * 32, 32);\n if (dstHeight % 32 !== 0) dstHeight = Math.max(Math.floor(dstHeight / 32) * 32, 32);\n const scaleWidth = dstWidth / srcWidth;\n const scaleHeight = dstHeight / srcHeight;\n\n return {\n srcHeight,\n srcWidth,\n dstHeight,\n dstWidth,\n scaleWidth,\n scaleHeight,\n };\n }\n\n /**\n * Run the detection model inference\n */\n private async runInference(\n tensor: Float32Array,\n resizeParams: ResizeParams\n ): Promise {\n const inputTensor = new this.ortModule.Tensor(\"float32\", tensor, [\n 1,\n 3,\n resizeParams.dstHeight,\n resizeParams.dstWidth,\n ]);\n\n const feeds = { x: inputTensor };\n const results = await this.session.run(feeds);\n const outputTensor = results[this.session.outputNames[0] || \"fetch_name_0\"];\n if (!outputTensor) {\n return null;\n }\n\n return outputTensor.data as Float32Array;\n }\n\n /**\n * Process detection results to extract bounding boxes\n */\n private postprocessDetection(\n detection: Float32Array,\n input: PreprocessDetectionResult,\n runtimeOptions: DetectionRuntimeOptions\n ): Box[] {\n const { dstWidth, dstHeight } = input.resizeParams;\n const greyImage = new Image(\n dstWidth,\n dstHeight,\n 1,\n new Uint8Array(detection.map((v) => Math.round(v * 255)))\n );\n const thresholdedImage = greyImage.threshold({\n threshold: 255 * runtimeOptions.textPixelThreshold,\n });\n const dilateImage = thresholdedImage.dilate({\n norm: \"LInf\",\n k: runtimeOptions.dilationKernelSize,\n });\n const boxes = dilateImage.contours({\n minArea: runtimeOptions.minimumAreaThreshold,\n });\n const finalBoxes = boxes.map((box) => {\n const paddedBox = this.applyPaddingToRect(box, dstWidth, dstHeight, runtimeOptions);\n const finalBox = this.convertToOriginalCoordinates(paddedBox, input.resizeParams);\n return finalBox;\n });\n return finalBoxes;\n }\n\n /**\n * Apply padding to a rectangle\n */\n private applyPaddingToRect(\n rect: { x: number; y: number; width: number; height: number },\n maxWidth: number,\n maxHeight: number,\n runtimeOptions: DetectionRuntimeOptions\n ) {\n const paddingVertical = runtimeOptions.paddingBoxVertical;\n const paddingHorizontal = runtimeOptions.paddingBoxHorizontal;\n const verticalPadding = Math.round(rect.height * paddingVertical);\n const horizontalPadding = Math.round(rect.height * paddingHorizontal);\n\n let x = rect.x - horizontalPadding;\n let y = rect.y - verticalPadding;\n let width = rect.width + 2 * horizontalPadding;\n let height = rect.height + 2 * verticalPadding;\n\n x = Math.max(0, x);\n y = Math.max(0, y);\n\n const rightEdge = Math.min(maxWidth, rect.x + rect.width + horizontalPadding);\n const bottomEdge = Math.min(maxHeight, rect.y + rect.height + verticalPadding);\n width = rightEdge - x;\n height = bottomEdge - y;\n\n return { x, y, width, height };\n }\n\n /**\n * Convert coordinates from resized image back to original image\n */\n private convertToOriginalCoordinates(\n rect: { x: number; y: number; width: number; height: number },\n resizeParams: ResizeParams\n ): Box {\n const scaledX = rect.x / resizeParams.scaleWidth;\n const scaledY = rect.y / resizeParams.scaleHeight;\n const scaledWidth = rect.width / resizeParams.scaleWidth;\n const scaledHeight = rect.height / resizeParams.scaleHeight;\n\n const x = Math.max(0, Math.round(scaledX));\n const y = Math.max(0, Math.round(scaledY));\n const width = Math.min(resizeParams.srcWidth - x, Math.round(scaledWidth));\n const height = Math.min(resizeParams.srcHeight - y, Math.round(scaledHeight));\n\n return { x, y, width, height };\n }\n}\n","import { DEFAULT_RECOGNITION_OPTIONS, DEFAULT_RECOGNITION_ORDERING_OPTIONS } from \"../constants.ts\";\nimport type {\n Box,\n OcrProgress,\n OrtInferenceSession,\n OrtModule,\n OrtTensor,\n RecognitionOptions,\n RecognitionOrderingOptions,\n RecognitionRuntimeOptions,\n} from \"../interface.ts\";\nimport type { Image } from \"../utils/image.ts\";\n\nexport interface RecognitionResult {\n text: string;\n box: Box;\n confidence: number;\n}\n\nexport interface SingleRecognitionTask {\n index: number;\n image: Image;\n box: Box;\n charWhiteSet?: Set;\n}\n\n/**\n * Service for detecting and recognizing text in images\n */\nexport class RecognitionService {\n private readonly options: RecognitionRuntimeOptions;\n private readonly session: OrtInferenceSession;\n private readonly ortModule: OrtModule;\n\n constructor(\n ortModule: OrtModule,\n session: OrtInferenceSession,\n options: Partial = {}\n ) {\n this.session = session;\n this.ortModule = ortModule;\n\n this.options = {\n ...DEFAULT_RECOGNITION_OPTIONS,\n ...options,\n };\n }\n\n /**\n * Main method to run text recognition on an image with detected regions\n * @param image The original image buffer or image in Canvas\n * @param detection Array of bounding boxes from text detection\n * @returns Array of recognition results with text and bounding box, sorted in reading order\n */\n async run(\n image: Image,\n detection: Box[],\n options?: RecognitionOptions\n ): Promise {\n const recognitionOptions = this.resolveRuntimeOptions(options?.recognition);\n const orderingOptions = this.resolveOrderingOptions(options?.ordering);\n const validBoxes = this.sortBoxesByReadingOrder(\n detection.filter((box) => box.width > 0 && box.height > 0),\n orderingOptions\n );\n const results: RecognitionResult[] = [];\n const charWhiteListSet = options?.charWhiteList?.length\n ? new Set(options.charWhiteList)\n : undefined;\n const total = validBoxes.length;\n const onProgress = options?.onProgress;\n\n onProgress?.({\n type: \"rec\",\n stage: \"start\",\n progress: this.createProgress(0, total),\n });\n\n for (const [i, box] of validBoxes.entries()) {\n const result = await this.processBox(\n {\n image: image,\n index: i,\n box: box,\n charWhiteSet: charWhiteListSet,\n },\n recognitionOptions\n );\n if (result) {\n results.push(result);\n }\n onProgress?.({\n type: \"rec\",\n stage: \"item\",\n progress: this.createProgress(i + 1, total),\n index: i,\n box,\n result: result ?? undefined,\n });\n }\n\n onProgress?.({\n type: \"rec\",\n stage: \"complete\",\n progress: this.createProgress(total, total),\n });\n\n return results;\n }\n\n private resolveRuntimeOptions(\n options: Partial = {}\n ): RecognitionRuntimeOptions {\n return {\n ...this.options,\n ...options,\n };\n }\n\n private resolveOrderingOptions(\n options: Partial = {}\n ): RecognitionOrderingOptions {\n return {\n ...DEFAULT_RECOGNITION_ORDERING_OPTIONS,\n ...options,\n };\n }\n\n /**\n * Process a single text box\n */\n private async processBox(\n task: SingleRecognitionTask,\n runtimeOptions: RecognitionRuntimeOptions\n ): Promise {\n const { image, box } = task;\n\n const crop = image.crop(box);\n const resizedCrop = crop.resize({\n height: runtimeOptions.imageHeight,\n });\n const tensor = resizedCrop.tensor({\n mean_values: runtimeOptions.mean,\n norm_values: runtimeOptions.stdDeviation,\n });\n\n const inputTensor = new this.ortModule.Tensor(\"float32\", tensor, [\n 1,\n 3,\n resizedCrop.height,\n resizedCrop.width,\n ]);\n const { data: outputData, dims: shape } = await this.runInference(inputTensor);\n\n const [, sequenceLength, numClasses] = shape;\n const { text: recognizedText, confidence } = this.ctcLabelDecode(\n outputData as Float32Array,\n sequenceLength,\n numClasses,\n runtimeOptions,\n task.charWhiteSet\n );\n\n return { text: recognizedText, box, confidence };\n }\n\n /**\n * Sort recognition results by reading order (top to bottom, left to right)\n */\n private sortBoxesByReadingOrder(\n boxes: Box[],\n orderingOptions: RecognitionOrderingOptions\n ): Box[] {\n if (!orderingOptions.sortByReadingOrder) {\n return [...boxes];\n }\n\n return [...boxes].sort((boxA, boxB) => {\n if (\n Math.abs(boxA.y - boxB.y) <\n (boxA.height + boxB.height) * orderingOptions.sameLineThresholdRatio\n ) {\n return boxA.x - boxB.x;\n }\n return boxA.y - boxB.y;\n });\n }\n\n private createProgress(current: number, total: number): OcrProgress {\n return {\n current,\n remain: total - current,\n total,\n };\n }\n\n /**\n * Runs the ONNX inference session with the prepared tensor\n */\n private async runInference(inputTensor: OrtTensor): Promise {\n const feeds = { x: inputTensor };\n const results = await this.session.run(feeds);\n\n const outputNodeName = Object.keys(results)[0];\n const outputTensor = results[outputNodeName];\n\n if (!outputTensor) {\n throw new Error(\n `Recognition output tensor '${outputNodeName}' not found. Available keys: ${Object.keys(results)}`\n );\n }\n\n return outputTensor;\n }\n\n private ctcLabelDecode(\n logits: Float32Array,\n sequenceLength: number,\n numClasses: number,\n runtimeOptions: RecognitionRuntimeOptions,\n charWhiteSet?: Set\n ): { text: string; confidence: number } {\n const dict = runtimeOptions.charactersDictionary;\n let text = \"\";\n const scores: number[] = [];\n\n let lastIndex = -1;\n\n for (let t = 0; t < sequenceLength; t++) {\n let maxScore = 0;\n let maxScoreIndex = 0;\n\n const offset = t * numClasses;\n for (let i = 0; i < numClasses; i++) {\n const val = logits[offset + i];\n if (val > maxScore) {\n maxScore = val;\n maxScoreIndex = i;\n }\n }\n\n if (maxScoreIndex === lastIndex) {\n continue;\n }\n\n lastIndex = maxScoreIndex;\n\n if (maxScoreIndex === 0) {\n continue;\n }\n\n const char = dict[maxScoreIndex] || \"\";\n\n if (charWhiteSet && !charWhiteSet.has(char) && char !== \" \") {\n continue;\n }\n\n text += char;\n scores.push(maxScore);\n }\n\n return {\n text,\n confidence:\n scores.length > 0\n ? scores.reduce((sum, score) => sum + score, 0) / scores.length\n : 0,\n };\n }\n}\n","import {\n DEFAULT_DETECTION_OPTIONS,\n DEFAULT_PADDLE_OPTIONS,\n DEFAULT_PROCESS_RECOGNITION_OPTIONS,\n DEFAULT_RECOGNITION_OPTIONS,\n DEFAULT_RECOGNITION_ORDERING_OPTIONS,\n} from \"../constants.ts\";\nimport type {\n DetectionRuntimeOptions,\n ImageInput,\n OrtInferenceSession,\n PaddleOptions,\n ProcessRecognitionOptions,\n RecognitionOptions,\n RecognitionOrderingOptions,\n RecognitionRuntimeOptions,\n} from \"../interface.ts\";\nimport { Image } from \"../utils/image.ts\";\nimport { DetectionService } from \"./detection.ts\";\nimport { type RecognitionResult, RecognitionService } from \"./recognition.ts\";\n\nexport interface PaddleOcrResult {\n text: string;\n lines: RecognitionResult[][];\n confidence: number;\n}\n\nexport interface FlattenedPaddleOcrResult {\n text: string;\n results: RecognitionResult[];\n confidence: number;\n}\n\n/**\n * PaddleOcrService - Provides OCR functionality using PaddleOCR models\n *\n * This service can be used either as a singleton or as separate instances\n * depending on your application needs.\n */\nexport class PaddleOcrService {\n options: PaddleOptions;\n\n detectionSession: OrtInferenceSession | null = null;\n detectionService: DetectionService | null = null;\n\n recognitionSession: OrtInferenceSession | null = null;\n recognitionService: RecognitionService | null = null;\n\n /**\n * Create a new PaddleOcrService instance\n * @param options Optional configuration options\n */\n constructor(options?: Partial) {\n if (!options?.ort) {\n throw new Error(\n \"PaddleOcrService requires the 'ort' option to be set with onnxruntime-node or onnxruntime-wen.\"\n );\n }\n this.options = {\n ...DEFAULT_PADDLE_OPTIONS,\n ...(options || {}),\n };\n }\n\n /**\n * Initialize the OCR service by loading models\n */\n public async initialize(): Promise {\n const ort = this.options.ort;\n if (!ort) {\n throw new Error(\n \"PaddleOcrService requires the 'ort' option to be set with onnxruntime-node or onnxruntime-wen.\"\n );\n }\n\n // Init detection service\n const detectionModelBuffer = this.options.detection?.modelBuffer;\n if (!detectionModelBuffer) {\n throw new Error(\n \"Detection model buffer is required. Please provide a valid ONNX model.\"\n );\n }\n this.detectionSession = await ort.InferenceSession.create(detectionModelBuffer);\n const { modelBuffer: _detectionModelBuffer, ...detectionOptions } =\n this.options.detection ?? {};\n this.detectionService = new DetectionService(ort, this.detectionSession, detectionOptions);\n\n // Init recognition service\n const recognitionModelBuffer = this.options.recognition?.modelBuffer;\n if (!recognitionModelBuffer) {\n throw new Error(\n \"Recognition model buffer is required. Please provide a valid ONNX model.\"\n );\n }\n this.recognitionSession = await ort.InferenceSession.create(recognitionModelBuffer);\n const { modelBuffer: _recognitionModelBuffer, ...recognitionOptions } =\n this.options.recognition ?? {};\n this.recognitionService = new RecognitionService(\n ort,\n this.recognitionSession,\n recognitionOptions\n );\n }\n\n /**\n * Check if the service is initialized with models loaded\n */\n public isInitialized(): boolean {\n return this.detectionSession !== null && this.recognitionSession !== null;\n }\n\n /**\n * Create a new instance instead of using the singleton\n * This is useful when you need multiple instances with different models\n * @param options Configuration options for this specific instance\n */\n public static async createInstance(options?: PaddleOptions): Promise {\n const instance = new PaddleOcrService(options);\n await instance.initialize();\n\n return instance;\n }\n\n private resolveDetectionRuntimeOptions(\n options: Partial = {}\n ): DetectionRuntimeOptions {\n const { modelBuffer: _modelBuffer, ...instanceOptions } = this.options.detection ?? {};\n\n return {\n ...DEFAULT_DETECTION_OPTIONS,\n ...instanceOptions,\n ...options,\n };\n }\n\n private resolveRecognitionRuntimeOptions(\n options: Partial = {}\n ): RecognitionRuntimeOptions {\n const { modelBuffer: _modelBuffer, ...instanceOptions } = this.options.recognition ?? {};\n\n return {\n ...DEFAULT_RECOGNITION_OPTIONS,\n ...instanceOptions,\n ...options,\n };\n }\n\n private resolveRecognitionOrderingOptions(\n options: Partial = {}\n ): RecognitionOrderingOptions {\n return {\n ...DEFAULT_RECOGNITION_ORDERING_OPTIONS,\n ...options,\n };\n }\n\n /**\n * Runs object detection on the provided image input, then performs\n * recognition on the detected regions.\n *\n * @param image - The raw image data as an ArrayBuffer or Canvas.\n * @param options - Optional configuration for the recognition output, e.g., `{ flatten: true }`.\n * @return A promise that resolves to the OCR result, either grouped by lines or as a flat list.\n */\n public async recognize(\n input: ImageInput,\n options?: RecognitionOptions\n ): Promise {\n if (!this.detectionService || !this.recognitionService) {\n throw new Error(\"PaddleOcrService is not initialized. Please call initialize() first.\");\n }\n const channels = input.data.length / (input.width * input.height);\n if (!Number.isInteger(channels) || channels < 1 || channels > 4) {\n throw new Error(\n `Invalid input data: ${input.data} for image size ${input.width}x${input.height}. Expected 1, 3, or 4 channels.`\n );\n }\n const detectionRuntimeOptions = this.resolveDetectionRuntimeOptions(options?.detection);\n const recognitionRuntimeOptions = this.resolveRecognitionRuntimeOptions(\n options?.recognition\n );\n const orderingOptions = this.resolveRecognitionOrderingOptions(options?.ordering);\n if (!recognitionRuntimeOptions.charactersDictionary?.length) {\n throw new Error(\n \"Recognition charactersDictionary is required. Provide it in createInstance({ recognition }) or recognize(_, { recognition }).\"\n );\n }\n let image = new Image(input.width, input.height, channels, input.data);\n\n const padding = detectionRuntimeOptions.padding;\n if (padding) {\n image = image.padding({\n padding,\n color: [255, 255, 255, 255],\n });\n }\n const detection = await this.detectionService.run(image, {\n ...detectionRuntimeOptions,\n onProgress: options?.onProgress,\n });\n const recognition = await this.recognitionService.run(image, detection, {\n ...options,\n detection: detectionRuntimeOptions,\n recognition: recognitionRuntimeOptions,\n ordering: orderingOptions,\n });\n\n return recognition;\n }\n\n /**\n * Processes raw recognition results to generate the final text,\n * grouped lines, and overall confidence.\n */\n processRecognition(\n recognition: RecognitionResult[],\n options?: ProcessRecognitionOptions\n ): PaddleOcrResult {\n const result: PaddleOcrResult = {\n text: \"\",\n lines: [],\n confidence: 0,\n };\n\n if (!recognition.length) {\n return result;\n }\n\n // Calculate overall confidence as the average of all individual confidences\n const totalConfidence = recognition.reduce((sum, r) => sum + r.confidence, 0);\n result.confidence = totalConfidence / recognition.length;\n const processOptions = {\n ...DEFAULT_PROCESS_RECOGNITION_OPTIONS,\n ...options,\n };\n\n let currentLine: RecognitionResult[] = [recognition[0]];\n let fullText = recognition[0].text;\n let avgHeight = recognition[0].box.height;\n\n for (let i = 1; i < recognition.length; i++) {\n const current = recognition[i];\n const previous = recognition[i - 1];\n\n const verticalGap = Math.abs(current.box.y - previous.box.y);\n const threshold = avgHeight * processOptions.lineMergeThresholdRatio;\n\n if (verticalGap <= threshold) {\n currentLine.push(current);\n fullText += ` ${current.text}`;\n\n avgHeight =\n currentLine.reduce((sum, r) => sum + r.box.height, 0) / currentLine.length;\n } else {\n result.lines.push([...currentLine]);\n\n fullText += `\\n${current.text}`;\n\n currentLine = [current];\n avgHeight = current.box.height;\n }\n }\n\n if (currentLine.length > 0) {\n result.lines.push([...currentLine]);\n }\n\n result.text = fullText;\n return result;\n }\n\n /**\n * Releases the onnx runtime session for both\n * detection and recognition model.\n */\n public async destroy(): Promise {\n await this.detectionSession?.release();\n await this.recognitionSession?.release();\n }\n}\n\nexport default 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