An Overview of Optical Character Recognition (OCR)
Introduction
Optical Character Recognition (OCR) is a powerful technology that has been proven to be a crucial component to a variety of companies. Actually the digital transformation requires the conversion of multiple images that contain text into text files. Therefore, it is evident that the use of an trustworthy OCR device is essential to information retrieval and communication.
Present OCR technology is often effective when it comes to documents that are in excellent condition (well-oriented and with sufficient light and contrast, with no defects in the image simple to comprehend writing style and font size, etc.). But the reality isn't 100% perfect. Many of the challenges OCR encounters occur when these conditions don't meet the requirements. Therefore there's a demand for well-constructed and efficient tools that can meet the widest range of options.
What exactly is OCR and how does it function?
OCR refers to the process by which a computer transforms an image that contains the text (typed or written) into a text file. It is generally the case regardless of what language used or the format. The task is carried out in the form of two steps: detecting text and recognition of the text. In the event of challenges (the problems we mentioned earlier) it is possible to take some initial actions to lessen the stress. The most popular ones are:
Skewing: by re-aligning and turning the document to allow for an analysis that is more standard
Despeckle: to eliminate possible parasite dots
The conversion: in grayscales or to binarization
Deblurring: and applying filters
Line removal for boxes: and other elements that aren't characters (e.g images, tables and lines that separate them, etc.)
Line detection
Isolating: this text box (or cropping)
Then the preprocessing is applied and it results in a more digital image. The second step is to detect text by placing bounding boxes over the words or sentences. The next step is to recognize the text in itself which could be character-by-character or in complete word (which could create a language-specific algorithm and therefore beneficial for specific use cases).
Another step could occur later on to process the collected Dataset For Machine Learning by OCR. OCR algorithm so that it can make corrections to errors. E.g when the word is not included to our dictionary, then we could substitute it with a similar word that needs to be changed by only a few characters.
What are the readily available OCR tools? How do we select the best one?
Many OCR options can be found, each having particular strengths and weaknesses. There are, in general, APIs and software that can be downloaded. Let's look at some of them here:
Cloud-based APIs
When working on a particular project, cost becomes part of the calculation and can limit the freedom to choose. This is why it is crucial to think about this issue because the APIs we be presenting in this section aren't open source. This is particularly relevant when the usage doesn't require specific capabilities or performances which are not available for free.
AWS Textract
Console interface (based on an Machine Learning algorithm) here also displays bounding boxes as well as the image's text.
Microsoft Azure Cognitive Services
To make use of this API, you need to sign up for an account with Azure: Cognitive Services. Artificial Intelligence tool of Azure: Cognitive Services. The implementation phase which follows to incorporate the API use within the code is fairly simple. The result of this implementation as well as the input image also have bounding boxes, as is the text.
IBM Datacap
This API includes some attractive and interesting options. Particularly the scanning mechanism and the process steps are simple. It also comes with a variety of options for customization, an effective OCR Training Dataset function, and is compatible with various types of devices and platforms. However, it's important to note that it's slow and its support for the UI isn't sufficient compared to other apps.
Benchmarking various OCR technologies
We'll refer you to Nanonets' blog which has the authors doing an extremely thorough job of comparison of several OCR tools using intriguing criteria. These criteria are a good summary essential aspects of evaluating the quality of an OCR Datasets. The final table of comparison is as follows:
Naturally, the assessment criteria used for this study place their solution in the forefront (which is intended to explain the difference in cost between Nanonets and others). Therefore it is necessary for more elements in order to identify the differentiators between OCR solutions. For instance, apart from the actual issues to take into consideration (pricing and ergonomy etc. ) One can also consider the precision of the proposed solution, which can be evaluated using different metrics. In real life, this precision measure is heavily dependent on the application however, there are some common metrics available. E.g: edit distances (Levenshtein distance, Damerau-Levenshtein distance (slightly different), Jaro-Winkler distance), Dynamic Time Warping, Hamming Distance, etc.
It is also essential to determine the appropriate guidelines when confronted with various OCR solutions, namely ones suitable to the specific situation. These guidelines can be used as general guidelines however, the selection will depend on the particular needs and limitations.
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