The OCR API supports a wide range of document types, including scanned documents, images, PDFs, and photographs. It accepts commonly used image formats such as JPEG, PNG, GIF, and TIFF, making it compatible with various sources of text-containing media.

Yes, the OCR API excels at recognizing handwritten text from documents and images. Leveraging advanced machine learning algorithms, it can decipher various handwriting styles and provide accurate text extraction, making it an ideal solution for digitizing handwritten notes and forms.

The OCR API is designed to process text in all languages spoken worldwide. Through its robust language models and character recognition capabilities, it accurately recognizes and extracts text in diverse languages, scripts, and character sets, making it suitable for international businesses with a global customer base.

The OCR API leverages cutting-edge technology to deliver high accuracy in text recognition. While it aims to provide precise results, the actual accuracy may vary depending on factors like image quality and handwriting legibility. Regular updates and continuous model refinement ensure constant improvements in accuracy.

The OCR API boasts low latency and high availability, ensuring swift processing of your requests. The actual response time may depend on factors like network speed and the complexity of the input document. However, you can expect fast and reliable results for most scenarios.

The OCR API returns structured data containing the extracted text from the provided image. The primary output includes the recognized text in a readable format, allowing users to easily access and utilize the information.

The key fields in the response data include "statusCode," indicating the success of the request, and "body," which contains the "fullText" field with the extracted text. This structure allows for straightforward interpretation of results.

The response data is organized in a JSON format, with a top-level object containing "statusCode" and "body." The "body" further includes the "fullText" field, which holds the extracted text, making it easy to parse and use in applications.

The OCR endpoint primarily accepts the "image URL" parameter, which specifies the location of the image from which text will be extracted. This allows users to customize their requests based on the source of the document.

Users can customize their data requests by providing different image URLs to extract text from various documents. Additionally, they can adjust the image quality and format to optimize recognition accuracy based on their specific needs.

The OCR API provides information extracted from images, including printed and handwritten text. This encompasses a wide range of documents, such as contracts, invoices, and historical manuscripts, making it versatile for various applications.

Data accuracy is maintained through advanced machine learning algorithms that continuously improve text recognition capabilities. Regular updates and model refinements ensure that the API adapts to diverse writing styles and formats for optimal performance.

Typical use cases for the OCR API include document digitization, multilingual customer support, data entry automation, and historical document analysis. These applications help streamline workflows and enhance data accessibility across various industries.

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