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 * Handler that receives OCR operation progress updates.

export declare type ProgressListener = (progress: number) => void;

 * Flags indicating position of a text item.

 * Keep this in sync with `LayoutFlags` in lib.cpp.

 * Item of text found in a document image by layout analysis.

    /** Combination of flags from {@link layoutFlags} */

 * Item of text found in a document image by layout analysis and OCR.

    /** Confidence score for this word in [0, 1] */

export declare type TextUnit = 'line' | 'word';

 * Low-level synchronous API for performing OCR.

 * Instances are constructed using {@link createOCREngine}.

     * Use {@link createOCREngine} rather than calling this directly.

     * @param tessLib - Emscripten entry point for the compiled WebAssembly module.

    constructor(tessLib: any, progressChannel?: MessagePort);

     * Shut down the OCR engine and free up resources.

     * Get the value, represented as a string, of a Tesseract configuration variable.

     * See {@link setVariable} for available variables.

     * Set the value of a Tesseract configuration variable.

     * For a list of configuration variables, see

     * https://github.com/tesseract-ocr/tesseract/blob/677f5822f247ccb12b4e026265e88b959059fb59/src/ccmain/tesseractclass.cpp#L53

     * If you have Tesseract installed locally, executing `tesseract --print-parameters`

     * will also display a list of configuration variables.

    setVariable(name: string, value: string): void;

     * Load a trained text recognition model.

    loadModel(model: Uint8Array | ArrayBuffer): void;

     * Load a document image for processing by subsequent operations.

     * This is a cheap operation as expensive processing is deferred until

     * bounding boxes or text content is requested.

    loadImage(image: ImageBitmap | ImageData, rectangle?: Rectangle): void;

    getTextBoxes(unit: TextUnit): TextItem[];

    getTextBoxes(unit: TextUnit, onProgress?: ProgressListener): TextItem[];

     * Perform layout analysis and text recognition on the current image, if

     * not already done, and return the page text as a string.

     * A text recognition model must be loaded with {@link loadModel} before this

    getText(onProgress?: ProgressListener): string;

     * Attempt to determine the orientation of the document image in degrees.

     * This currently uses a simplistic algorithm [1] which is designed for

     * non-uppercase Latin text. It will likely perform badly for other scripts or

     * [1] See http://www.leptonica.org/papers/skew-measurement.pdf

 * Return true if the current JS runtime supports all the WebAssembly features

 * needed for the "fast" WebAssembly build. If not, the "fallback" version must

export declare function supportsFastBuild(): boolean;

export declare type CreateOCREngineOptions = {

 * Initialize the OCR library and return a new {@link OCREngine}.

export declare function createOCREngine({ wasmUrl, wasmFallbackUrl, }?: CreateOCREngineOptions): Promise<OCREngine>;

  "description": "OCR library built on Tesseract",

    "url": "git+https://github.com/robertknight/tesseract-wasm.git"

  "author": "Robert Knight <robertknight@gmail.com>",

    "url": "https://github.com/robertknight/tesseract-wasm/issues"

  "homepage": "https://github.com/robertknight/tesseract-wasm#readme",

    "@rollup/plugin-commonjs": "^22.0.0",

    "@rollup/plugin-node-resolve": "^13.3.0",

    "@rollup/plugin-typescript": "^8.3.2",

		{
		"name": "@mateuszp/tesseract-wasm",
		"version": "0.9.48",
		"version": "0.9.51",
		"description": "OCR library built on Tesseract",
		@@ -5,0 +5,0 @@ "type": "module",

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