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OCR Custom Object Extraction Document

Optical Character Recognition (OCR) has become an integral part of digital transformation, opening doors to convert raw data from documents into easily processed information. However, in this ever-evolving world, the need for custom object extraction from documents is becoming increasingly pressing. Let’s delve deeper into how OCR can be customized to extract specific objects from documents and how this can result in high-accuracy data.

Example Document: Bpkb, SIUP, NPWP, Buku Rekening Tabungan, SNTK, Etc.

Why Customizing OCR is Crucial?

  1. Business-specific Needs:
    • Every industry has unique documents and information that require custom object extraction. By customizing OCR, organizations can meet their specific needs.
  2. High Accuracy:
    • Customization allows for improved extraction accuracy, as the system can focus on specific types of data and disregard other elements.
  3. Operational Efficiency:
    • By customizing OCR to extract only relevant information, organizations can reduce the amount of data that needs manual processing, enhancing operational efficiency.

Steps in Creating OCR Custom Object Extraction

  1. Identify Business Needs:
    • Review the documents to be extracted and identify the custom objects needed.
    • Consider document format, text variations, and languages used.
  2. Choose a Customizable OCR Platform:
    • Select an OCR platform that allows the use of custom models or algorithms.
    • Ensure the platform can handle document and language variations.
  3. Collect and Annotate Data:
    • Gather a dataset that represents the variations of objects to be extracted.
    • Annotate the data with information about the objects to be extracted.
  4. Train Custom OCR Model:
    • Use the dataset to train a custom OCR model.
    • Monitor and evaluate the model during training to ensure good accuracy.
  5. Validation and Adjustments:
    • Validate the model using unseen test data.
    • Adjust the model based on validation results to improve performance.
  6. Integration with Existing Systems:
    • Integrate the custom OCR model with existing systems or applications.
    • Test the integration to ensure good interoperability.

Benefits and Challenges

Benefits:

  • High extraction accuracy.
  • Improved operational efficiency.
  • Adaptation to business needs.

Challenges:

  • Requires resources for model training.
  • Needs regular maintenance and updates.
  • Challenges in handling format and language variations.

Applicable:

  • Financial Industry.
  • Otomotive.
  • Hospitalitry.

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