5 Best Practices For Data Capture Using OCR

One of the greatest methods for automatic data capture is OCR (optical character recognition). With the aid of this technology, data may be quickly and effectively extracted from a variety of information sources, including documents. OCR technology is used by data entry firms to extract data from files in any kind of electronic or paper format and then transform this data into the necessary electronic format. The OCR technique includes turning scanned images of typewritten or handwritten text into computer text. OCR-enabled automated data input programmes aid in optimising templates for data reformatting from scanned documents. The system also works to transform the data into searchable and editable formats, making it simpler to retrieve the material in the future.

Using OCR technology has several benefits. Businesses benefit from increased productivity and effectiveness of work. Its ability to quickly search through enormous content is helpful for office environments that deal with large document intake and high volume scanning. Other advantages of using OCR for automated data capture include higher productivity, lower costs, high accuracy, fewer mistakes, more storage space, greater data security, complete text searchability of digitised documents, improved customer service, and data security even in emergencies.

According to a Transparency Market Research (TMR) analysis, the global OCR market is anticipated to grow at a CAGR of 14.8% from 2017 to 2025, reaching a value estimate of US $25.1 billion at that time.

A Guide to OCR-based Data Capture Best Practices

Start from the Base-  Analyze the data in the printed source material from the ground up. The data entry team should do this. The quality of data capture may be impacted by aspects such as paper quality, language, font, layout, and graphical elements. Additionally, this will give the data entry team information they may use to assess how readily they can complete the data capture. For instance, lexical information is needed for OCR data entry on historical documents. Similar to this, it may be necessary to take specific steps to prepare image-rich documents for OCR or it may be necessary to use improvised OCR data collection to properly digitise the papers.

Set Up OCR Project Goals-  To Establish OCR Project Goals Every OCR-based data capture project needs to have distinct, well-defined goals. The data entry team must choose the approach that best meets the project’s needs and can produce the kind of output for data capture that is necessary. Depending on the degree of precision required for the work, the post-OCR output can also need additional manual correction or processing. Here are a few important factors to think about to achieve the objectives of the OCR data collection project.

Identifying the necessary output type and its function

determining the degree of data capture accuracy.

Does the data capture project require text-only data capture, or should extra components be added for enhanced searchability in addition to the text?

What is the user’s level of error tolerance?

Whether consumers would need to see the OCR text files displayed

Have a Well-charted Work Flow-  The success or failure of the OCR data input endeavour may not be determined by a well-charted process workflow. Instead, a carefully planned flow ensures that data collection and conversion go as planned.OCR

Perform OCR Quality Check Processes-  Quality assurance (QA) procedures must be implemented as a level of control in every successful data capture project. The data collection project will be on track and the goals will be met in the allotted period thanks to this QA programme. The QA team should conduct a thorough examination of all of the acquired data or a segmented examination. QA methods also involve monitoring and fixing data entry problems. A knowledgeable OCR service provider can complete all of these tasks effectively. It is crucial to make sure that the QA plan is correctly developed, and put into practice, and that all staff members are informed of the quality requirements.

Be adaptable to changes in project size and cost.

Be Flexible to Project Scale and Cost Variations-  OCR-based data capture initiatives may have different requirements depending on the project. Additionally, there could be unforeseen changes that arise during the project that calls for adjustments. As a result, data entry teams must consider some degree of flexibility while developing their OCR data capture plans to deal with and adjust as necessary. Planning at the proper level is necessary since changes in scale may affect project budgets and timetables. The difference between anticipated and actual expenditures for an OCR data collection activity can be substantial. In actuality, the prices might be larger. The duration of the OCR data capture project may change due to unforeseen and unplanned circumstances. But with careful planning, time can be set aside for these possibilities.

OCR data collection can be made effective and reliable by working with a data entry provider with experience with the technology. For more clear information you can also visit:      https://it-s.com/

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