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10 Examples of OCR Implementation in Business Data Management

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Data is a valuable asset that helps companies make better decisions in their operations. As a result, the data processing must be carried out accurately and precisely.

To increase the quality of output generated by data processing, many companies entrust the data processing process itself by utilizing Optical Character Recognition (OCR) technology.

In this article, we will explain the definition of OCR, how it works, what data can be processed using OCR, and examples of its implementation in business data processing.

What is OCR and its function

OCR, which is short for Optical Character Recognition, is a technology designed to recognize text or characters in an image or document that has been scanned or photographed.

OCR works to convert text in images into editable and searchable text. In other words, OCR helps in the conversion of non-data text into text data that can be used and managed.

In the business world, this technology can clearly save time and labor in the process of data input to data management.

Before OCR existed, administrative or accounting personnel input data from hundreds of transaction receipts into the system by manual typing. But now all processes of scanning transaction proof can be done automatically and directly entered into the company database.

How OCR works

Before going into more examples of its implementation in business data processing, it’s a good idea to first know how OCR technology works.

In general, OCR works by identifying patterns and character forms in scanned images or documents. The following are the general steps in the OCR work process:

  1. Image scan: document or image taken by a scanner or digital camera
  2. Pre-processing: The preprocessing process involves noise removal, contrast improvement, and color adjustments to ensure clearer images.
  3. Segmentation: OCR divides the image into smaller parts, such as characters or words.
  4. Character recognition: OCR analyzes every segment and tries to identify what characters are in it.
  5. Convert to text: character that is identified, then converted into editable text
  6. Post-processing: OCR results may require minor manual refinement to ensure data accuracy and quality.

Data that can be processed with OCR

Business data sometimes comes in a variety of formats. Fortunately, OCR is not limited only to plain text documents but can also be used for other formats. OCR technology can be used to process various types of business data, including:

  1. Text document: such as a letter, invoice, and contract
  2. Image: OCR can be used to recognize text in images such as a card name or handwritten document.
  3. Barcode: A barcode and QR code can be scanned and parsed into meaningful data.
  4. Signature: OCR can be used to recognize and process signatures on documents.
  5. Table-based data: OCR can be used to extract data from a table in a document

10 examples of OCR implementation in business data management

After knowing the definition and how it works, the following are some examples of the implementation of OCR in business data management that we often encounter.

1. Recording of invoices and receipts

Businesses often receive many invoices and receipts. By using OCR, important data such as the date, amount, and company name can be extracted automatically. This has certainly helped speed up the accounting process and minimize errors made by manual workers.

The best OCR, for example, is Fintelite’s OCR+. OCR+ can extract documents such as invoices, receipts, bills, and other physical proof of financial transactions into digital text data that can be inputted.

With OCR+, extracted data can be automatically grouped based on category, so that data processing at the end of the period becomes easier and faster.

2. Processing of incoming mail

In large companies, documents often come in various formats, such as letters, faxes, or emails. OCR can be used to recognize text in this document and categorize or store it as needed.

3. Text recognition on an image

Many business data is in the form of images, such as graphics, charts, or scanned documents. OCR can be used to extract text from this image, which makes the analysis process faster.

4. Identity verification of the customer

In the banking or security industries in particular, OCR can be used to read and compare identification documents such as an identity card or passport with data input by customers to verify their identity.

5. Signature recognition

For businesses that require customer signatures, OCR can be used to recognize signatures automatically, verify the validity of documents, and minimize the risk of fraud.

6.Handwritten recognition

Not only signatures, but OCR can also be used to recognize handwritten We can see an example of this application in the health industry, where OCR is used to process data on doctors’ prescriptions or medical records, which can reduce the risk of data input errors.

7. Market data analysis

In stock trading or investment, OCR can be used to automatically recognize and process incoming news or reports so that investors can respond quickly to market changes.

8. Barcode recognition for shipping goods

E-commerce and logistics businesses frequently use OCR to read barcodes on products or shipments to ensure accuracy in order processing and delivery.

9. Convert the document to a digital format

OCR can be used to convert physical documents, such as books or paper archives, into digital formats that are more easily searchable and indexed.

10. Analysis of customer data

Customers are frequently asked to fill out a customer satisfaction form. By using OCR in a survey form or questionnaire, businesses can quickly analyze customer feedback and identify trends or problems that need to be addressed.

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The implementation of OCR in business data management can save time

That was all the information about 10 examples of OCR implementation in business data management. Based on these examples, we agree that using OCR technology can save time, reduce human error, and improve overall operational efficiency.

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