mirror of
https://github.com/gchq/CyberChef.git
synced 2024-11-16 17:08:31 +01:00
95 lines
3.3 KiB
JavaScript
95 lines
3.3 KiB
JavaScript
/**
|
|
* @author n1474335 [n1474335@gmail.com]
|
|
* @author mshwed [m@ttshwed.com]
|
|
* @author Matt C [me@mitt.dev]
|
|
* @copyright Crown Copyright 2019
|
|
* @license Apache-2.0
|
|
*/
|
|
|
|
import Operation from "../Operation.mjs";
|
|
import OperationError from "../errors/OperationError.mjs";
|
|
import { isImage } from "../lib/FileType.mjs";
|
|
import { toBase64 } from "../lib/Base64.mjs";
|
|
import { isWorkerEnvironment } from "../Utils.mjs";
|
|
|
|
import Tesseract from "tesseract.js";
|
|
const { createWorker } = Tesseract;
|
|
|
|
import process from "process";
|
|
|
|
/**
|
|
* Optical Character Recognition operation
|
|
*/
|
|
class OpticalCharacterRecognition extends Operation {
|
|
|
|
/**
|
|
* OpticalCharacterRecognition constructor
|
|
*/
|
|
constructor() {
|
|
super();
|
|
|
|
this.name = "Optical Character Recognition";
|
|
this.module = "OCR";
|
|
this.description = "Optical character recognition or optical character reader (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text.<br><br>Supported image formats: png, jpg, bmp, pbm.";
|
|
this.infoURL = "https://wikipedia.org/wiki/Optical_character_recognition";
|
|
this.inputType = "ArrayBuffer";
|
|
this.outputType = "string";
|
|
this.args = [
|
|
{
|
|
name: "Show confidence",
|
|
type: "boolean",
|
|
value: true
|
|
}
|
|
];
|
|
}
|
|
|
|
/**
|
|
* @param {ArrayBuffer} input
|
|
* @param {Object[]} args
|
|
* @returns {string}
|
|
*/
|
|
async run(input, args) {
|
|
const [showConfidence] = args;
|
|
|
|
if (!isWorkerEnvironment()) throw new OperationError("This operation only works in a browser");
|
|
|
|
const type = isImage(input);
|
|
if (!type) {
|
|
throw new OperationError("Invalid File Type");
|
|
}
|
|
|
|
const assetDir = isWorkerEnvironment() ? `${self.docURL}/assets/` : `${process.cwd()}/src/core/vendor/`;
|
|
|
|
try {
|
|
self.sendStatusMessage("Spinning up Tesseract worker...");
|
|
const image = `data:${type};base64,${toBase64(input)}`;
|
|
const worker = createWorker({
|
|
workerPath: `${assetDir}tesseract/worker.min.js`,
|
|
langPath: `${assetDir}tesseract/lang-data`,
|
|
corePath: `${assetDir}tesseract/tesseract-core.wasm.js`,
|
|
logger: progress => {
|
|
if (isWorkerEnvironment()) {
|
|
self.sendStatusMessage(`Status: ${progress.status}${progress.status === "recognizing text" ? ` - ${(parseFloat(progress.progress)*100).toFixed(2)}%`: "" }`);
|
|
}
|
|
}
|
|
});
|
|
await worker.load();
|
|
self.sendStatusMessage("Loading English language...");
|
|
await worker.loadLanguage("eng");
|
|
self.sendStatusMessage("Intialising Tesseract API...");
|
|
await worker.initialize("eng");
|
|
self.sendStatusMessage("Finding text...");
|
|
const result = await worker.recognize(image);
|
|
|
|
if (showConfidence) {
|
|
return `Confidence: ${result.data.confidence}%\n\n${result.data.text}`;
|
|
} else {
|
|
return result.data.text;
|
|
}
|
|
} catch (err) {
|
|
throw new OperationError(`Error performing OCR on image. (${err})`);
|
|
}
|
|
}
|
|
}
|
|
|
|
export default OpticalCharacterRecognition;
|