In this article, we will explore exactly what zonal OCR is and its importance. We also share our OCR zone process. Read on to learn more.
Zonal OCR (Optical Character Recognition) is a technology that extracts text from specific, predefined areas of a document. It is commonly used to automate data entry by capturing information from various documents such as forms and invoices.
Example: Data extraction using zone OCR can be used to extract invoice numbers from specific areas of scanned documents, such as pulling "INV-2023" from the top-right corner of an Amazon invoice. This technology streamlines data entry by automatically identifying and capturing key information like order numbers and totals.
Use our 10 step OCR data extraction process for zones to efficiently manage data from various documents. Simply follow the steps below:
Identify and mark specific areas on the document where data needs to be extracted. These zones correspond to fields like invoice numbers, dates, or amounts.
Example: Mark the top-right corner of an Amazon invoice to capture the invoice number "INV-2023."
Use a scanner or an imaging device to convert physical documents into digital format. Ensure the quality of the scanned images is high for better OCR accuracy.
Example: Scan a batch of utility bills using a high-resolution scanner to create clear digital copies.
Enhance the scanned images by removing noise, correcting skew, and adjusting contrast. This step improves the accuracy of the OCR process.
Example: Use image preprocessing software to correct skew in a scanned image of a tax form, making text more legible.
Use OCR software to recognize and convert text within the defined zones into machine-readable data. The software processes each zone separately to extract the relevant information.
Example: Apply Tesseract OCR to extract the date from a predefined zone on a scanned medical report.
Check the extracted data for accuracy and completeness. This step may involve automated checks or manual verification.
Example: Validate the extracted invoice number "INV-2023" against a list of known invoice numbers to ensure correctness.
Export the validated data into a usable format such as CSV, Excel, or directly into a database. This makes the data accessible for further processing or analysis.
Example: Export the extracted invoice numbers and amounts from multiple invoices into an Excel spreadsheet for accounting purposes.
Integrate the extracted data into existing business systems like ERP or CRM for seamless workflow automation. This allows for real-time data updates and improved operational efficiency.
Example: Automatically upload the extracted invoice details into the company's SAP ERP system for financial processing.
Store the scanned and processed documents in a secure digital archive. This ensures easy retrieval and compliance with record-keeping regulations.
Example: Save all scanned invoices and their extracted data in a secure cloud storage solution for future reference.
Regularly monitor the OCR process and make improvements as needed. This may involve updating the OCR software, refining zone definitions, or enhancing preprocessing techniques.
Example: Adjust the OCR zone definitions and update the software to improve extraction accuracy for new invoice templates.
Set up automated workflows to handle repetitive tasks and ensure consistent data extraction. This can include scheduled scans, automatic preprocessing, and data validation routines.
Example: Schedule daily scans of incoming mail, automatic preprocessing, and extraction of key data fields into the company database.
MedTech Solutions aims to improve its document processing to enhance operational efficiency and maintain high standards of data accuracy. Here's how they implemented our simple 10-step process:
Identify and mark specific areas on medical documents where data needs to be extracted. For instance, mark the top-right corner of a patient admission form to capture the medical record number "MRN-2024."
Use a scanner or an imaging device to convert physical medical records into digital format. For example, scan a batch of patient consent forms using a high-resolution scanner to create clear digital copies.
Enhance the scanned images by removing noise, correcting skew, and adjusting contrast. For instance, use image preprocessing software to correct skew in a scanned image of a medical report, making text more legible.
Use OCR software to recognize and convert text within the defined zones into machine-readable data. Apply Tesseract OCR to extract the date from a predefined zone on a scanned medical report.
Check the extracted data for accuracy and completeness. Validate the extracted medical record number "MRN-2024" against a list of known medical record numbers to ensure correctness.
Export the validated data into a usable format such as CSV, Excel, or directly into a database. For example, export the extracted patient names and diagnosis codes from multiple forms into an Excel spreadsheet for further analysis.
Integrate the extracted data into existing business systems like EMR (Electronic Medical Records) for seamless workflow automation. Automatically upload the extracted patient details into the company's EMR system for efficient patient management.
Store the scanned and processed documents in a secure digital archive. Save all scanned patient forms and their extracted data in a secure cloud storage solution for future reference and compliance.
Regularly monitor the OCR process and make improvements as needed. Adjust the OCR zone definitions and update the software to improve extraction accuracy for new medical document templates.
Set up automated workflows to handle repetitive tasks and ensure consistent data extraction. Schedule daily scans of incoming patient records, automatic preprocessing, and extraction of key data fields into the company database.
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Here are some of the most common benefits of implementing OCR zone recognition:
Improved Text Recognition Accuracy: Using OCR zones allows for targeted recognition, which significantly improves accuracy by focusing on specific areas of a document. This approach minimizes errors caused by irrelevant or extraneous content, ensuring that only the necessary text is captured and processed.
Increased Document Processing Speed: By defining specific zones for OCR, the software can quickly identify and extract text from designated areas, reducing the time required for full-page scanning and processing. This efficiency is especially beneficial for high-volume document processing tasks.
Enhanced Data Organization and Management: OCR zones help in structuring and categorizing data by isolating different types of information within a document. This facilitates easier data extraction, indexing, and retrieval, improving overall document management and accessibility.
Cost Efficiency in Document Handling: Targeted OCR reduces the need for extensive manual data entry and correction, leading to cost savings in labor and resources. Efficient processing also lowers the operational costs associated with large-scale document management systems.
Scalability for Varying Document Volumes: Implementing OCR zones allows for scalable document processing solutions, accommodating varying volumes and types of documents without compromising on accuracy or speed. This flexibility supports business growth and adaptation to changing data management needs.
Customizable Processing for Diverse Documents: OCR zones can be tailored to the specific layout and structure of different documents, providing a customized solution that enhances the overall effectiveness of the OCR process. This adaptability ensures optimal performance across diverse document types.
We hope that you now have a better understanding of what zone OCR is and how to use our 10 step process for extracting data using zone optical character recognition. If you enjoyed this article, you might also like our article on how to improve OCR accuracy or our article on OCR vs. ICR.