Optical character recognition is one technology that modern-day businesses cannot thrive without. What is also worth noticing is that optical character recognition is an umbrella term that comprises several small procedures. There are many components and methodologies involved in optical character recognition, owing to which we get our final output as a digitally converted text. In this article, we shall take a look at the text detection component of the OCR technology and also compare it with text recognition. Let us get started.
One of the first few steps of optical character recognition is text detection. Here, text detection refers to the practice of identifying characters in a given text sequence and then enclosing it with a rectangular box.
This process to detect text in images is automated using the different types of optical character recognition software and applications. The automation gets fulfilled using an algorithm, which may be an image based-algorithm or a frequency-based algorithm, respectively. Image segmentation, which involves dividing the entire image into several different regions, requires an image-based algorithm for processing.
● Image-based Algorithms
Image-based algorithms of text detection check whether two pixels have similar quality and parameters or not. If the parameters match, then they are placed in a single segment which further makes it easier for the software to detect and extract characters. These parameters are nothing but the statistical features assigned to every character.
Further, if machine learning models are utilized for OCR technology, every model is trained for a diverse set of statistical parameters. It is based on the statistical parameters that the final shape and resemblance of the extracted text will be determined. So, the right algorithm which works towards providing an accurate output is an absolute necessity at this point.
● Frequency-based Algorithms
Discrete Fourier Transform In Text Detection
The discrete Fourier transform is a fundamental mathematical model which is quite widely used in digital signal processing. The frequency-domain representation of any signal or input is obtained using the discrete Fourier transform. The problem with large-scale image processing is that an image is quite a big entity if you consider all its pixels in totality. So, having an automated representation of all frequencies of the image is not possible.
Therefore, discrete Fourier transform is useful because it brings forward a set of samples that is large enough to accurately describe the characteristics of the text or image.
Discrete Wavelet Transform (DWT) In Text Detection
Another popular signal processing methodology, which is again a frequency-based algorithm is the Discrete Wavelet Transform (DWT). The basic function of the algorithm is to decompose a given text input into a number of readable sets. When sets are formed in this way, each of them is assigned a series of coefficients which makes it easier to detect the text input as a whole.
In a nutshell, the algorithm works by representing any given signal in the most redundant form. With such preconditioning of the signal, it becomes relatively easier to detect texts with much higher accuracy.
● Region-based Text Detection Approach
A region-based text detection approach also works well, if there is a clear demarcation between textual and non-textual components. It can’t be used for specific needs like to detect names in text. There are several documents where the frequency of non-textual entities is also very high.
If the main goal of automated data extraction is to focus on textual characters only, without anything to do with images, tables, etc., a traditional region-based approach for text detection is also good enough. The use of a 64 x 32-pixel window for binding textual regions for detection is the most common method.
How Does Text Detection Differ from Recognition?
Text recognition differs from text detection very minutely. Once the text has been detected using an algorithm as mentioned above, it is now time to transform every character into a string or a meaningful sentence. This actually emulates the human brain in making sense out of several different characters that are written together.
Data parsing is loosely based on the same principle. Text recognition, therefore, involves the conversion of detected text into words and finally into meaningful sentences that are available to the end-user in a digitally usable form.
Image processing text recognition also has many different techniques with which the job is completed. Character recognition and word recognition are two different methodologies for this process. Character recognition will involve the separation of the text image detected so far into single character cutouts.
As per the name, OCR involves this strategy itself. The detected text images are first divided into K classes, which are further processed using the binary text image hypothesis.
KlearStack OCR Solution
KlearStack has been a strong force in the OCR technology development domain. Our Optical character recognition software involves smooth text detection and recognition, both of which get completed within a few seconds. The highlight of our OCR solution is the incorporation of artificial intelligence methodologies.
By empowering the simple traditional OCR concept with artificial intelligence and machine learning, we ensure that outputs are always accurate and that any template does not hinder the possibility of extracting the text out of a given document. To know more about what we do and how our application works, contact KlearStack for a free demo.