5 Best Practices for OCR Based Data Capture

Jack Taylor
August 2, 2022
 Mins Read

OCR is one of the most popular technologies used for data capture as it helps to capture data in a quick and efficient way from different sources of information including documents. OCR technology is used to convert various kinds of text and image data into machine-readable information. Availing the services of an experienced OCR outsourcing service provider can reduce the amount of time required for carrying out data entry and help businesses focus on their core processes.

With all its ease of use, a lack of awareness of good practices for data capture using OCR can lead to more errors. Here are certainly good practices that can help in ensuring efficient and consistently high-quality capture of data using OCR:

5 Best Practices for OCR-Based Data Capture

1. Start at the Root:

Data entry teams can save themselves a lot of trouble by undertaking a thorough analysis of the printed source material. Details such as paper quality, characteristics such as language, font, layout, and graphical elements are critical features that may affect the quality of data capture. This will provide information that will help to determine whether the data capture can be easily achieved or whether it is more difficult than expected. For example, certain kinds of historical documents may lack the lexical data that is required for OCR data entry and this can pose challenges. Image-rich documents may need special measures to render them OCR compatible or may need improvised OCR data capture. An experienced data entry outsourcing service provider will be able to provide the necessary expertise in this regard.

2. Establish OCR Project Goals:

Any OCR-based data capture project must have specific goals in place. Depending upon the purpose of your project, you may have to find the best method that suits you and delivers the kind of data capture output required. If the outcome were meant to be text files that can be used by a search engine, then the OCR text-based output would be good enough. Post-OCR output may also require further manual correction or processing based on the level of accuracy required for the work. Therefore, some of the key flag posts, which can be used for concreting your OCR data capture project goals are:

  • Determining the kind of output required and for what purpose
  • Establishing the level of accuracy of data capture
  • Does your data capture project require text-only data capture or needs to include other elements that would be used along with the text for more powerful searchability? In which case you may need to use data in more powerful formats such as XML or SGML.
  • What is the error tolerance of the user? You would need to ascertain the client’s quality expectations in this regard.
  • Whether the OCR text files would need to be displayed to users?

3. OCR Process Work Flow:

It cannot be emphasized enough that having a well-delineated process workflow will determine the success or failure of your OCR data entry effort. A well-charted flow will ensure that your data capture and conversion succeed as per your expectations.

4. OCR Quality Check Procedures:

All successful OCR data capture projects require the implementation of quality assurance procedures as a level of control. This QA program will ensure that the data capture project is on track and that the goals are achieved within the desired time. Depending on your budgetary allocation, you may consider getting your QA team to carry out a full-fledged review of the entire captured data or a sectional review. QA procedures also include tracking and rectification of data capture errors. All these can be done competently by an experienced OCR-based data clean-up services provider. Ensure that your QA strategy is rigorously worked out and implemented and the quality standards are communicated to all staff involved.

5. Be Responsive To Project Scale and Cost Variations:

The demands of OCR-based data capture projects may vary from project to project. Not only that, there may be changes that happen during the course of the project which may necessitate modifications. Hence, teams may need to keep in mind a certain amount of flexibility when coming up with their OCR data capture plans so that they can tackle and adapt accordingly. Changes in scale may influence project schedules and budgets and hence need an appropriate level of planning. Similarly, the expected costs of carrying out an OCR data capture task and actual costs incurred may vary widely. The actual costs may tend to be higher. OCR data capture project duration may lengthen or shorten owing to unexpected and unplanned reasons. Advanced planning will ensure the allocation of time for these possibilities so that they can be taken into account and needed adjustments are done.

OCR data capture is a quick and effective way of managing large volumes of data accumulated because of business and organizational processes and transactions. A certain kind of foresightedness, understanding of project goals, and excellent planning are required to ensure that OCR-based data capture projects are carried out properly. Leveraging the services of a data capture services expert would help companies use the power of OCR to enhance their business efficiency.

Article by
Jack Taylor

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