How to convert a scanned image into text using C# in .NET Framework

Extracting text from a scanned document has never been easier. Forget all the troublesome coding you were preparing yourself for. Today we are going to look at how to set up this functionality in just a few minutes. How about we get right to it?

First off, we need to run the following command in the Package Manager console to install our API:

Install-Package Cloudmersive.APIClient.NET.OCR -Version 2.1.4

Now that that is out of the way, we can call ImageOcrPost, as you can see demonstrated below. It is much more accurate if you specify a target language.

using System;
using System.Diagnostics;
using Cloudmersive.APIClient.NET.OCR.Api;
using Cloudmersive.APIClient.NET.OCR.Client;
using Cloudmersive.APIClient.NET.OCR.Model;
namespace Example
{
public class ImageOcrPostExample
{
public void main()
{
// Configure API key authorization: Apikey
Configuration.Default.AddApiKey("Apikey", "YOUR_API_KEY");
// Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
// Configuration.Default.AddApiKeyPrefix("Apikey", "Bearer");
var apiInstance = new ImageOcrApi();
var imageFile = new System.IO.Stream(); // System.IO.Stream | Image file to perform OCR on.  Common file formats such as PNG, JPEG are supported.
var recognitionMode = recognitionMode_example;  // string | Optional; possible values are 'Basic' which provides basic recognition and is not resillient to page rotation, skew or low quality images uses 1-2 API calls; 'Normal' which provides highly fault tolerant OCR recognition uses 14-16 API calls; and 'Advanced' which provides the highest quality and most fault-tolerant recognition uses 28-30 API calls.  Default recognition mode is 'Advanced' (optional)
var language = language_example;  // string | Optional, language of the input document, default is English (ENG).  Possible values are ENG (English), ARA (Arabic), ZHO (Chinese - Simplified), ZHO-HANT (Chinese - Traditional), ASM (Assamese), AFR (Afrikaans), AMH (Amharic), AZE (Azerbaijani), AZE-CYRL (Azerbaijani - Cyrillic), BEL (Belarusian), BEN (Bengali), BOD (Tibetan), BOS (Bosnian), BUL (Bulgarian), CAT (Catalan; Valencian), CEB (Cebuano), CES (Czech), CHR (Cherokee), CYM (Welsh), DAN (Danish), DEU (German), DZO (Dzongkha), ELL (Greek), ENM (Archaic/Middle English), EPO (Esperanto), EST (Estonian), EUS (Basque), FAS (Persian), FIN (Finnish), FRA (French), FRK (Frankish), FRM (Middle-French), GLE (Irish), GLG (Galician), GRC (Ancient Greek), HAT (Hatian), HEB (Hebrew), HIN (Hindi), HRV (Croatian), HUN (Hungarian), IKU (Inuktitut), IND (Indonesian), ISL (Icelandic), ITA (Italian), ITA-OLD (Old - Italian), JAV (Javanese), JPN (Japanese), KAN (Kannada), KAT (Georgian), KAT-OLD (Old-Georgian), KAZ (Kazakh), KHM (Central Khmer), KIR (Kirghiz), KOR (Korean), KUR (Kurdish), LAO (Lao), LAT (Latin), LAV (Latvian), LIT (Lithuanian), MAL (Malayalam), MAR (Marathi), MKD (Macedonian), MLT (Maltese), MSA (Malay), MYA (Burmese), NEP (Nepali), NLD (Dutch), NOR (Norwegian), ORI (Oriya), PAN (Panjabi), POL (Polish), POR (Portuguese), PUS (Pushto), RON (Romanian), RUS (Russian), SAN (Sanskrit), SIN (Sinhala), SLK (Slovak), SLV (Slovenian), SPA (Spanish), SPA-OLD (Old Spanish), SQI (Albanian), SRP (Serbian), SRP-LAT (Latin Serbian), SWA (Swahili), SWE (Swedish), SYR (Syriac), TAM (Tamil), TEL (Telugu), TGK (Tajik), TGL (Tagalog), THA (Thai), TIR (Tigrinya), TUR (Turkish), UIG (Uighur), UKR (Ukrainian), URD (Urdu), UZB (Uzbek), UZB-CYR (Cyrillic Uzbek), VIE (Vietnamese), YID (Yiddish) (optional)
var preprocessing = preprocessing_example;  // string | Optional, preprocessing mode, default is 'Auto'.  Possible values are None (no preprocessing of the image), and Auto (automatic image enhancement of the image before OCR is applied; this is recommended). (optional)
try
{
// Convert a scanned image into text
ImageToTextResponse result = apiInstance.ImageOcrPost(imageFile, recognitionMode, language, preprocessing);
Debug.WriteLine(result);
}
catch (Exception e)
{
Debug.Print("Exception when calling ImageOcrApi.ImageOcrPost: " + e.Message );
}
}
}
}

And that’s it! All text will be extracted from the image using optical character recognition.