What is OCR? – Diggernaut can help!

1 min read

What is OCR? - Diggernaut can help!

Optical character recognition (OCR) is a technology that accurately recognizes printed and written text characters by a computer. It might involve photo scanning of the text, analysis of the scanned-in image and the conversion of a character image to character codes. OCR software is also used to extract data from scanned documents or camera images that enables the user to access and edit the content of the original document. In comparison with other prevalent techniques for automatic identification that may include speech recognition, radio frequency identification and bar code reader, OCR is unique as it does not require control of the process that produces the information.

In recent time, OCR technology has been applied across various industry verticals, thereby revolutionizing the entire document management process. Major industries where OCR technology is widely used include banking, legal, healthcare, education, finance, and government. During the past several years, the OCR technology has come a long way, from a special purpose reader to multi-purpose interactive system.
This advancement has eventually lowered the data capturing cost and has led to the development of more reliable OCR system. Therefore, OCR technology may become an advantageous solution for businesses that require a lot of paper documentation or have large historical data that needs to be digitized.

The OCR market is driven by the requirement of accuracy and speed in the enterprises. What were typical hurdles in the past including typographical and formatting complexities are presently being overcome by the recognition feature that most of the OCR software has.

However, the most visible hurdle in the growth of this technology is the lack of recognition of handwritten material. Presently the standard OCR software accurately works for a standardized set of documents.

Nevertheless, a more advanced OCR technology might include intelligent character recognition (ICR) that works on the learning model of human brains which can eventually solve the problem of recognition of complex handwritten documents. The growing demand for data analytics may create a massive opportunity for the market to grow.

A typical OCR system consists of several components which may include optical scanning, location segmentation, preprocessing, feature extraction and recognition post-processing. Through the scanning process, the digital image of the original document is captured. The segmentation process determines the constituent of the image, and when applied to text it helps in the isolation of character or words. Some of the defects resulting from the scanning process which may cause poor recognition rate is eliminated using preprocessing to smoothen the digitized character. Feature extraction captures the essential character of the symbol while the recognition post-processing further authenticates the document by processing each sentence at a time. The sophistication of the OCR system depends on the type and the number of font recognition. It defines the capabilities of an OCR system. For instance, OCR machine falling in the category of fixed font deals with the recognition of one specific typewritten form on the other hand OCR machine falling in the category of multi-font recognizes more than one font. An uni-font OCR system recognizes font without having to maintain a huge database of the specific font information.

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