Knowing how OCR software works is an intriguing subject in itself. While Optical character recognition has become an inevitable part of work culture, it is surprising how very few people understand how this technology works. In essence, OCR involves the conversion of scanned images, handwritten material, and printed text into computer-readable forms.
However, it is a very superficial representation of how the technology actually works. Therefore, to clear the air around the mechanism, how OCR software works, we decided to break down its working concepts for you in this article. Stick with us till the end to explore everything about the working of the OCR technology.
Starting from the basics, optical character recognition is aimed at digitizing all forms of the text so that it can be searched and edited electronically via computers. This way, important data becomes digitally accessible, more compact for easy storage, and can even be uploaded online.
The biggest reason why people are so interested in optical character recognition technology is that it significantly reduces the need for manual document handling. Just scan and convert the data into machine-encoded text, and then store it as compact files on your devices. Now, let’s take a look at how OCR engine works.
OCR is a very popular topic of research in the fields of artificial intelligence (AI), machine learning (ML), data extraction, pattern recognition, etc. Therefore, new kinds of OCR software are being developed now and then, which have slightly different working mechanisms from each other. Some common steps involved in the working of almost all types of OCR software are:
During the scanning process, if the alignment of the document or image is not correct in reference to the scanner itself, the text lines can be overlapping or skewed. To correct this, the scanned image has to be rotated or straightened a bit so that the horizontal or vertical alignment of the text lines becomes perfect. This process is called De-skewing.
The de-speckling process involves the removal of any kinds of unwanted positive or negative spots on the scanned image. It is important to remove them so that the final machine-encoded text that you receive does not get disarranged with the presence of these disturbances.
Simply speaking, binarization is the process in which the scanned image is converted into a black and white form. Principally, converting any multi-tonal picture into a bi-level document image makes the final conversion task a lot simpler. All colors in an image are defined by their pixels. The binarization process in OCR scanning commonly works by the threshold mechanism.
In very simple terms, with binarization, you can play with the contrast of the image, generally after converting the image in grey scale. Once the image is converted to grey scale, you can then choose to increase or reduce the contrast. You can make shades closer to white convert into pure white and shades closer to black convert to pitch black. The decision of which shades should be converted to white and black is decided by the threshold value.
A threshold is set such that any pixel value identified above this level will be treated as white, and similarly, pixels below the level will be considered black. Quite naturally, the entire process depends on determining the threshold value. All characteristics of the image like- contrast, exposure, sharpness, etc., have to be studied before selecting the threshold value.
For a more accurate OCR detection, any vertical or horizontal line panning across the black and white document image has to be removed. Proper image processing for removing noisy lines is instrumental in providing clear digital text in the end.
Zoning involves the analysis of the layout of the scanned image. The entire document is divided into zones and attributes are identified in each zone. These include zones of alphanumeric characters, numeric characters, graphics, images, etc. Applications that provide features of Optical Character Recognition are capable of automatic zoning. Zoning is a crucial pre-process for those documents that have multi-column layouts or several non-textual characters as well.
For this, firstly all the words are identified and a box is created around them. Subsequently, every text line is identified. This is followed by the identification of the first letter of every line and so on. This preprocessing method helps in ensuring that the final text has a proper alignment.
When a document contains text printed or written in multiple languages, preprocessing is needed to identify each character that belongs to a different language compared to the others.
As the name suggests, segmentation involves breaking down the entire document into subparts. It is important to understand segmentation to learn how optical character recognition or OCR software works. Each of these subparts is then analyzed individually. Common steps involved in this process are Line Level Segmentation, Word Level Segmentation, Character Level Segmentation, etc.
Finally, all different aspects of the image are converted into a range of pixels so that they appear familiar to the software. This is called Normalization.
The main concept to understand how OCR software works is the extraction of data or features. This process can take place in two possible ways. The first method is where the software recognizes specific features to identify each character. For instance, if the letter A is written somewhere in the text, the software can identify it, irrespective of the font style or size, by simply recognizing two lines angled against each other and a horizontal line present between them. This technique is called Feature Detection.
The second technique is called Pattern Recognition. Here, characters are identified on the whole, by recognizing the pattern after comparing them with the stored information. If letter A in the document resembles the stored form of A, it will be recognized instantly.
No OCR extraction is hundred percent perfect. Some final processing needs to be done to make the digitized data more relevant.
For error correction, every incorrect word is first expanded, and then an automated dictionary lookup is performed. Finally, the corrected form is extracted and the wrong word is replaced.
Conventional OCR technology was a breakthrough invention. However, a lot of glitches and challenges had to be tackled. With continuous research, modern forms of OCR are being developed that have effectively dealt with all these obstacles. OCR was one of the earliest domains of research in Artificial Intelligence. Today, AI has transformed how OCR software works, and continues to make it better.
KlearStack realizes the potential of AI for changing the OCR experience completely. Our AI-backed OCR solutions perform accurate character extraction, and deep learning allows them to detect and correct errors in no time. To take a demo of this advanced OCR scanning, contact KlearStack today.