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An Overview of Optical Character Recognition (OCR) in 2021



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November 5, 2021

Optical Character Recognition (OCR) is an incredible innovation that has shown to be a critical component to many organizations. Indeed, their digital transition progress requires the transformation of a few pictures containing text occurrences into text reports. In this way, clearly having a solid OCR tool is essential for data recovery and communication.

Current OCR innovations are frequently very incredible with regards to records that come in great conditions (all around situated with sufficient light and contrast, no flaws87 in the picture, easy to use and understand writing style, and so forth) In any case, the fact of the matter is a long way from being awesome. To be sure, many difficulties that OCR faces emerge when these conditions don’t make a difference. Accordingly, there is a requirement for robust and well-performing instruments across the range of conceivable outcomes.

 

What is OCR and how can it function?

OCR is the point at which a machine converts over a picture containing text (composed or manually written) into a text document. By and large, it happens to pay little heed to the language or the organization. This undertaking is acted in a two-step process: distinguishing text and perceiving the said text. Nonetheless, despite difficulty (the difficulties we clarified above), we can play out some starter activities to mitigate them. The most well-known ones are:

  • Skewing: re-adjusting and pivoting the record for a more standardized analysis
  • Despeckle: to eliminate conceivable parasite spots
  • Converting to grayscale or binarization
  • Deblurring and applying filters
  • Line deletion for boxes and elements that don’t establish characters (e.g.: tables, pictures, isolated lines, and so forth)
  • Line location
  • Pre-segregating the text box (or editing)

To start with, we apply this pre-processing, and the outcome is a simpler to-digitize picture. Second, message location happens, setting jumping boxes on the sentences or words. Then, at that point, comes the ID of the actual text, which can either happen character by character or by entire words (which would make the calculation language-specific and would thus be able to be helpful for specific use cases).

Last, another progression can come later to post-process the yield of the OCR algorithm to address botches. E.g.: If a word doesn’t have a place in the word reference, we can supplant it with a nearby word that requires changing a few characters.

 

What are the available OCR tools and how would we pick the most fitting one?

A few OCR solutions are accessible, each with its qualities and specificities. Basically, there are downloadable programming and APIs. How about we examine some of them here:

 

Cloud-Based APIs

When chipping away at a task, cost turns out to be essential for the situation and may control the opportunity of decision. As a result, it is fundamental to consider this factor since the APIs we will introduce in this segment are not open-source. This is particularly significant when the utilization case doesn’t need explicit abilities/exhibitions that are not openly accessible.

 

Google Cloud Vision

Being a finished bundle that is viable with other Google services, this API offers an OCR administration, among others. It naturally returns the jumping boxes encompassing the text and the text anticipated whenever given a picture.

Note: Google Docs additionally offers a free OCR tool to change Pdf reports over to text. Be that as it may, it doesn’t change over tables and footnotes.

Pros:

  • Set-up is easy
  • Generally better performance than other APIs

Cons:

  • Documentation not up-to-date
  • Installing several packages on the user’s local machine required
  • Non-customizable features

Pricing:

  • 1$50/1000 pages for 5 million pages or less
  • 0$60/1000 pages for more than 5 million pages

 

AWS Textract

The console interface (based on a machine learning algorithm) here also returns the bounding boxes and the text given an image.

Pros:

  • Flexible pricing
  • Ease of use after set-up

Cons:

  • Relatively tedious to set-up
  • Requires several steps (downloading packages and various files essentially)
  • Not suited for handwritten documents

Pricing:

  • 1$50/1000 pages for 1 million pages or less
  • 0$60/1000 pages for more than 1 million pages.

 

Microsoft Azure Cognitive Services

To use this API, one needs to create an account on the artificial intelligence tool of Azure: Cognitive Services. Fortunately, the implementation part that comes next to include the API usage in the code is rather easy. The resulting output from this implementation and the input image are also bounding boxes and the contained text.

Pros:

  • Easy implementation after set-up
  • Over 100 languages are available
  • Compatible with Docker usage

Cons:

  • Requires a credit card addition for the free trial (privacy issue)

Pricing:

  • 1$/transaction for 1 to 1 million transactions
  • 0$65/transaction for 1 million to 10 million transactions
  • 0$60/transaction for 10 to 100 million transactions
  • 0$40/ transaction for more than 100 million transactions

 

IBM Datacap

This API has some strangely appealing components. Specifically, the checking system and the handling steps are fairly simple. It likewise offers numerous adjustable elements, a solid OCR capacity, and similarities with various stages and devices. However, it is worth focusing on that it is slow and the help on the UI isn’t adequate compared with its rivals.

Pros:

  • Simple scanning and processing mechanisms
  • Customizable features
  • Strong OCR function
  • Compatibility with different platforms and devices

Cons:

  • Slow processing
  • Insufficient support on the UI

Pricing: variable, depends on the use case (number of requests, bandwidth, etc.)
For further custom comparisons of the tools aforementioned, you can try with a few documents on this comparison platform.

 

ABBYY Finereader

ABBYY has been providing companies with OCR tools for a long time. Although it has presented several software solutions to tackle it, we will only focus on Finereader here (the others may be previous versions or offer different features).

Pros:

  • Ergonomic interface
  • Keyboard-friendly correction feature
  • Buy-only-once software
  • Decent accuracy

Cons:

  • No merging of various documents
  • Outputs might require some post-processing.

Pricing: 199$ for the standard version for Windows and 129$ for MacOS.

 

Adobe Acrobat Pro DC

Adobe Acrobat has been unknowingly offering an OCR service for quite some time. It comes as one of the best ones overall for PDF solutions. However, it is only available as an additional feature for Adobe Acrobat PDF reader.

Pros:

  • Supports multiple formats (inputs and outputs)
  • Ease of use
  • Compatible with Acrobat’s PDF handling features

Cons:

  • Heavy on the system and the storage
  • Does not come separately from the Acrobat PDF reader

Pricing: 15$/month for the Standard Plan

 

Tesseract

It is by far the most popular open-source OCR library. Developed by Hewlett-Packard, it was later (and up to today) maintained by Google

Pros:

  • A large panel of languages
  • Various output formats
  • Long-Shot-Term-Memory based models
  • Trainable

Cons:

  • Might not be suited for specific client use cases

Pricing: Free

 

SimpleOCR

SimpleOCR is a freeware bound for individual utilize that offers an SDK for engineers just as a wide word reference to which custom words can be added. It additionally offers the chance of handling a few archives simultaneously just as a spelling check.

Pros:

  • Wide updatable dictionary (more than 120k words)
  • Ability to process many documents simultaneously

Cons:

  • Does not offer (in the free version) a command-line interface
  • Cannot be deployed to several servers (for the free version)

Pricing: Free (paying versions also exist as a one-time-payment, starting from 25$)

Several other tools that are worth mentioning exist on the market, each with its strengths and weaknesses, such as Rossum, OmniPage, Klippa, Readiris, Docparser, Veryfi, and Hypatos.

 

Conclusion

All in all, it is very simple these days to track down a decent OCR arrangement that can answer a project’s requirements. A few arrangements can be more important than others, contingent upon the utilization case. Remember the genuine target of utilizing OCR in a given project and get derive evaluation, metrics from it.


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