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OCR
HINGLISH Ocr Model

HINGLISH Ocr Model

Extracts hindi and english text from images

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What is HINGLISH Ocr Model ?

The HINGLISH OCR Model is a specialized Optical Character Recognition (OCR) solution designed to extract text from images containing both Hindi and English languages. It is tailored for bilingual documents, scans, or photographs, making it ideal for users needing to process mixed-language content. The model also includes a feature to highlight search terms within the extracted text, enhancing usability for specific keyword searches.

Features

• Bilingual Support: Extracts text from images containing both Hindi and English scripts. • Mixed Language Handling: Seamlessly processes documents with intermixed Hindi and English text. • Search Term Highlighting: Highlights specific search terms within the extracted text for easy reference. • Image Compatibility: Works with various image formats, including JPEG, PNG, and BMP. • Customizable Accuracy: Allows users to adjust settings for improved text recognition in low-quality images.

How to use HINGLISH Ocr Model ?

  1. Install or Access the Model: Download the HINGLISH OCR Model API or use it via a cloud-based platform.
  2. Upload or Provide Image: Input the image containing Hindi and English text.
  3. Highlight Search Terms: Enter specific keywords or phrases to highlight in the extracted text.
  4. Run Extraction: Execute the OCR process to extract text from the image.
  5. Review Results: View the extracted text with highlighted search terms for easy analysis.

Frequently Asked Questions

What types of images can the HINGLISH OCR Model process?
The model supports various image formats, including JPEG, PNG, BMP, and TIFF. It works best with clear, high-resolution images.

Does the HINGLISH OCR Model require an internet connection?
Yes, if using the cloud-based API. Offline functionality depends on the specific deployment method.

How accurate is the HINGLISH OCR Model in extracting text?
Accuracy depends on image quality. Clear images with legible text yield the best results. Adjusting settings can improve accuracy for low-quality images.

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