5 Best Practices for OCR Based Data Capture

Today data can be stored and transferred in various formats such as word.doc, PDF, codes, mails, text messages, images, web content, videos etc.  Nonetheless, most data is still filed as hand written documents, files, letters etc. Various approaches are available to capture, transform and store the aforementioned data. One of the most common methodologies used in data capture is OCR or Optical Character Recognition where different kinds of text and image data are converted into machine readable formats. OCR are effective when large volume of data has to be managed and transferred, especially in large scale industries such as hospitals, legal firms etc. However, lack of OCR run-through and awareness may lead to errors and inefficient data capture. Hence, here are 5 Best Practices for OCR Based Data Capture.

Practices for Data Capture Using OCR

1. Make the Root Strong

You can save time if you thoroughly analyse the materials that has to be transformed such as paper quality, language, font, graphics etc. This helps determine the time, resource and quality of end result. The analysis is most effective especially when capturing historical documents which lack lexical data that is vital in OCR methodology, significantly.Similarly, image based documents require different measures to suit OCR compatibility. All these have to be check listed appropriately.

2. Determine OCR Goals

Depending upon the project purpose you have to set your OCR data capturing goal. Determination of the approach is very important as few conversions may also require manual correction or processing post OCR capture. Certain factors that need to be flagged off while setting the goals are;

• Ascertaining the purpose and output required
• Forming the accuracy level of data capture
• The elements required in data capture. In such scenarios more formats such as XML and SGML can be used.
• Understanding client’s quality expectations and overall error tolerance percentage

3. Create an OCR Process Flow

If you want a smooth OCR data capturing operation, it paramount that you create a process flow of the work. A well-delineated process flow actually helps define the ratio of success or failure of the given project. Your efforts will not be wasted if you are aware as to how the project is going to work out. A process chart will also aid in projecting OCR data capturing project and your overall expectations.

4. Flexibility in Project Scale and Cost Variations

The expectations and end results differ depending on individual projects. Therefore, it is very important that the data capture team is flexible to adapt to changes and modifications in their project implementation. OCR Data Capturing scales may adversely affect project schedules and overall budget. Similarly additional cost may be charged for untimely expectations from clients. So for all this contingencies it is advisable to have contingency plans and flexible project duration.

5. Implementation of Quality Assurance

Quality is the foremost aspect one has to include in the OCR data capturing project. Each project should be set under a quality assurance procedure so as to ensure the projects are on right track and shall be completed on stipulated time period. Also, QA procedure is incorporated to evaluate the end results including review, correction and rectification and grading. Each project head should circulate a standard Quality Assurance Rule for better OCR project management.