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Aspose. OCR Cloud is a simple ocr technology, which you can use in your application to convert image to text. Add OCR to your app with simply few line of code. Any language, any platform, no hardware resources. Automatic skew correction and advanced automative image pre-processing: Aspose.OCR Cloud can successfully read poor quality scans and automatically make image pre-processing to correct low resolution, low contrast, noise and skew, image upscale resolution. 45 languages supported including Chinese. Automated and manual document layout detection: Recognize documents with complex layouts in fully automatic mode or with manual corrections. In automated mode, you don’t need to worry about text layout editing. Our DSR algorithm provides such functionality. It allows performing the OCR operation quickly on various document scans. Receipt recognition. Text region detection method Table image recognition. PDF recognition. Multipage supported. Text correction using spell checkong algorithms. Various Output formats: Text, Searchable PDF, hOCR. Supported image formats: We support all widely used image formats: bmp jpeg jpg jpe jp2 png webp tiff tif exr hdr pic svg Recognize Text of Different Languages: Supports 45 languages including: English, German, French, Italian, Spanish, Portuguese, Polish, Slovene, Slovak, Netherlands, Lithuanian, Latvian, Danish, Norwegian, Finnish, Serbian, Croatian, Czech, Swedish, Estonian, Romanian, Chinese, Arabic, Hindi, Russian, Ukrainian, Bengali, Tibetan, Thai, Urdu, Turkish, Korean, Indonesian, Hebrew, Javanese, Greek, Japanese, Persian, Albanian, Latin, Vietnamese, Uzbek, Georgian, Bulgarian, Azerbaijani Recognize english handwritten text. High speed with no hardware resources: Don’t worry about hardware, all the computational operations are performed at our powerful servers, that hosts our core, built on cutting edge fast neural networks.
Aspose.OCR Cloud is an optical character recognition as a service. With it, you can easily add OCR functionality to almost any device or platform, including netbooks, mini PCs, or even entry-level smartphones.
Our engine can read text from images, photos, screenshots and scanned PDFs in a wide variety of European, Cyrillic and Oriental fonts, returning results in the most popular document formats. Powerful built-in image processing filters based on neural networks automatically correct skewed and distorted images, automatically remove dirt, smudges, scratches, glare and other image defects that can affect recognition accuracy. To further improve the results, Aspose.OCR Cloud has a built-in spell checker that automatically replaces misspelled words and saves you the trouble of manually correcting the recognition results.
Even the complex recognition tasks can be done with a couple of API calls. To make interacting with Aspose.OCR Cloud services from .NET applications even easier, we provide the software development kit (SDK) for .NET. It handles all the routine operations such as establishing connections, sending API requests, and parsing responses, wrapping all these tasks into a few simple classes.
Aspose.OCR Cloud SDK for .NET is open source under the MIT license. You can freely use it for any projects, including commercial and proprietary applications, as well as modify any part of its code.
Image to Text | Image to Searchable PDF | PDF OCR | Receipt Scanner |
---|---|---|---|
![]() | ![]() | ![]() | ![]() |
Aspose.OCR Cloud is an on-demand service with a free tier. In order to use Aspose.OCR Cloud service, you must create an account at Aspose Cloud API:
Aspose values your privacy and takes technical, security and organizational measures to protect your data from unauthorized use, accidental loss or disclosure. Read our Privacy Policy and Terms of Service for details.
Aspose.OCR Cloud follows industry standards and best practices to keep your data secure. All communication with OCR REST API is done using JWT authentication, which provides an open-standard, highly secure way to exchange information. Time-limited JWT tokens are generated using Client ID and Client Secret credentials that are specific for each application. To obtain the credentials:
Sign in to Aspose Cloud API Dashboard.
Go to Applications page.
Click Create New Application button.
Give the application an easily recognizable name so it can be quickly found in a long list, and provide an optional detailed description.
Create the cloud storage by clicking the plus icon and following the required steps. You can also reuse existing storage, if available.
Aspose.OCR Cloud uses its own internal storage, so you can provide the bare minimum storage options:
Click Save button.
Click the newly created application and copy the values from Client Id and Client Secret fields.
Pass in the values from the Client ID and Client Secret fields when initializing the required OCR API.
A summary of recent changes, enhancements and bug fixes in Aspose.OCR Cloud SDK for .NET 24.11.2 release:
Key | Summary | Category |
---|---|---|
OCR-3977 | Added recognition of the Uyghur language, including mixed Uyghur/English texts. | New feature |
OCR-3978 | Added recognition of the Telugu language, including mixed Telugu/English texts. | New feature |
OCR-3979 | Added recognition of the Kannada language, including mixed Kannada/English texts. | New feature |
OCR-3980 | Added recognition of the Tamil language, including mixed Tamil/English texts. | New feature |
OCR-3985 | Added recognition of the Devanagari-based scripts, including mixed Devanagari/English texts. | New feature |
OCR-3986 | Added support for mixed Arabic/English texts. | Enhancement |
OCR-3984 | Added support for mixed Japanese/English texts. | Enhancement |
OCR-3983 | Added support for mixed Korean/English texts. | Enhancement |
OCR-3982 | Added support for mixed Chinese/English texts. | Enhancement |
OCR-3987 | Added support for mixed Persian/English texts. | Enhancement |
REST API changes: https://releases.aspose.cloud/ocr/release-notes/2024/aspose-ocr-cloud-24-11-0-release-notes/
This section lists all public API changes introduced in Aspose.OCR Cloud SDK for .NET 24.11.2 that may affect the code of existing applications.
The following public APIs have been added in Aspose.OCR Cloud SDK for .NET 24.11.2 release:
The following recognition languages have been added:
Script | REST API |
---|---|
Uyghur | Language.Uyghur |
Telugu | Language.Telugu |
Kannada | Language.Kannada |
Tamil | Language.Tamil |
Devanagari-based languages | Language.Devanagari |
All of the OCR languages mentioned above also support the recognition of mixed texts, including those with Latin characters.
No changes.
No changes.
This section lists all improvements introduced in Aspose.OCR Cloud SDK for .NET 24.11.2 that do not affect the public APIs and thus do not require changes to your application code.
Aspose.OCR Cloud now supports the recognition of texts which include both native and Latin characters for the following languages:
Language.Arabic
)Language.Chinese
)Language.Japanese
)Language.Korean
)Language.Persian
)The examples below illustrate the changes introduced in version 24.11.2:
using Aspose.OCR.Cloud.SDK.Api;
using Aspose.OCR.Cloud.SDK.Model;
using System.Text;
namespace Example
{
internal class Program
{
static void Main(string[] args)
{
/** Authorize your requests to Aspose.OCR Cloud API */
RecognizeImageApi recognizeImageApi = new RecognizeImageApi("<Client Id>", "<Client Secret>");
/** Read source image to array of bytes */
byte[] image = File.ReadAllBytes("source.png");
/** Specify recognition language */
OCRSettingsRecognizeImage recognitionSettings = new OCRSettingsRecognizeImage {
Language = Language.Tamil,
ResultType = ResultType.Text
};
/** Send image for recognition */
OCRRecognizeImageBody source = new OCRRecognizeImageBody(image, recognitionSettings);
string taskID = recognizeImageApi.PostRecognizeImage(source);
/** Fetch recognition result */
OCRResponse result = recognizeImageApi.GetRecognizeImage(taskID);
Console.WriteLine(Encoding.UTF8.GetString(result.Results[0].Data));
}
}
}
Find more information on Aspose.OCR Cloud and get professional help:
FAQs
Aspose. OCR Cloud is a simple ocr technology, which you can use in your application to convert image to text. Add OCR to your app with simply few line of code. Any language, any platform, no hardware resources. Automatic skew correction and advanced automative image pre-processing: Aspose.OCR Cloud can successfully read poor quality scans and automatically make image pre-processing to correct low resolution, low contrast, noise and skew, image upscale resolution. 45 languages supported including Chinese. Automated and manual document layout detection: Recognize documents with complex layouts in fully automatic mode or with manual corrections. In automated mode, you don’t need to worry about text layout editing. Our DSR algorithm provides such functionality. It allows performing the OCR operation quickly on various document scans. Receipt recognition. Text region detection method Table image recognition. PDF recognition. Multipage supported. Text correction using spell checkong algorithms. Various Output formats: Text, Searchable PDF, hOCR. Supported image formats: We support all widely used image formats: bmp jpeg jpg jpe jp2 png webp tiff tif exr hdr pic svg Recognize Text of Different Languages: Supports 45 languages including: English, German, French, Italian, Spanish, Portuguese, Polish, Slovene, Slovak, Netherlands, Lithuanian, Latvian, Danish, Norwegian, Finnish, Serbian, Croatian, Czech, Swedish, Estonian, Romanian, Chinese, Arabic, Hindi, Russian, Ukrainian, Bengali, Tibetan, Thai, Urdu, Turkish, Korean, Indonesian, Hebrew, Javanese, Greek, Japanese, Persian, Albanian, Latin, Vietnamese, Uzbek, Georgian, Bulgarian, Azerbaijani Recognize english handwritten text. High speed with no hardware resources: Don’t worry about hardware, all the computational operations are performed at our powerful servers, that hosts our core, built on cutting edge fast neural networks.
We found that aspose.ocr-cloud demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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