The Biggest Problem With OCR API And How You Can Fix It?

OCR API

OCR API (Optical Character Recognition) is a technology that recognizes, organizes, and extracts data found in printed books, handwritten documents, or photographs. The technology also increases data collection efficiency and reduces data extraction errors.

Using an OCR SDK, you can even extract data from blurry or out-of-focus images. However, OCR API technology is by no means perfect – yet. 

In this post, we’ll look at an OCR SDK, its capabilities, and how you can use Filestack’s OCR SDK to meet your data capture needs. 

Let’s dive right in.

What Is OCR SDK?

The Optical Character Recognition Software Development Kit (OCR SDK) boosts data extraction quality and production time. 

It allows you to interpret, organize, and extract data from scanned documents automatically, eliminating the need for manual data input. 

Is OCR API Technology Solving Problems?

Many people believe that OCR API solves our problems. Yet, the technologies available to us as open-source tools or from technical giants are far from ideal — they are excessively inflexible, frequently wrong, and fail in the real world.

In addition, many OCR APIs only solve a small number of use cases and are resistant to changes. However, there are some APIs evolving along with current data extraction needs, and Filestack is one of the best. 

What Technological Advances Led to Today’s OCR API Technology?

What Are Computer Vision Technologies?

OCR detects characters one by one using computer vision technologies. Following that, picture classification identifies each character. OCR produces accurate results if you do these two steps correctly. 

However, characters can sometimes run into each other during processing, making them illegible. As a result, OCR requires more than just computer vision. This is where NLP, or natural language processing, comes in. 

What Is Natural Language Processing (NLP)?

Even though OCR recognizes characters, those characters are used to create incorrect words, phrases, and paragraphs. These errors make entire documents illegible. 

However, natural language processing (NLP) research yields a slew of algorithms that can be utilized to rectify character recognition errors using probabilistic methods. Missing characters, for example, can be corrected using context.

What Is Supervised Machine Learning?

OCR uses deep learning techniques to improve its performance. While improving OCR API performance needs learning from training samples, this technique allows OCR systems to: 

  • Recognize characters in a variety of fonts. Each character can be written in various ways, and OCR software can detect the characters despite font changes thanks to a big annotated data set.
  • Recognize and correct faults. Characters that OCR techniques cannot detect can be skipped. OCR can detect faults and repair inaccuracies through pattern recognition in training samples.

What Are the Biggest Problems with OCR API? 

In human-machine communication, OCR is not a stand-alone solution. OCR’s fundamental flaw is that it only produces unstructured characters. 

As a result, you need other machine learning methods to integrate into OCR. Users can only access structured data from their documents this way.

Why Are OCR Solutions Still Not Applicable in Every Industry?

OCR solutions aren’t applicable in every industry because they frequently introduce errors in documents. Some of the most common OCR processing errors include:

  • Misinterpreting letters 
  • Skipping over illegible characters 
  • Mixing text from adjacent columns or image captions. 

While many factors influence OCR tool performance, you can determine the number of errors by the quality and shape of the text and the font.

Even the best OCR APIs can make errors when processing high-quality documents if each character appears in different formats, fonts, or styles. 

What Are the Limitations of an OCR API?

OCR APIs struggle to interpret the text correctly when scanning documents with: 

  • Colored Backgrounds – Colorful backdrop patterns might be distracting because they can make it difficult to read text.
  • Blurry Texts – Blurriness makes it difficult to create an accurate textual interpretation.
  • Skewed or Non-Oriented Documents – If the image is tilted, OCR software will find it challenging to identify the characters since they’re not aligned.

What Are the Text Limitations of an OCR API?

OCR APIs have the following text-based limitations:

  • Letter Variation – Some alphabets have more difficult-to-recognize letterforms. Arabic language character recognition, for example, is complex because even these characters are written in cursive.
  • A Wide Range of Font Types and Sizes – While it can be difficult for OCR APIs to distinguish between all of the different font types, the technology also finds it challenging to differentiate between too small or too large characters.
  • Lookalike Characters – Some characters are so similar that OCR software may not be able to tell them apart. The software finds it challenging to tell the difference between the number “0” and the letter “O,” for example.
  • Handwritten Text – As everyone writes characters differently, OCR software may not be able to identify similar characters written in different ways. 

What Is the Best Way to Assess the Accuracy of an OCR API?

The percentage of characters in a text that the OCR tool can extract without errors measures OCR accuracy. For instance, 99 percent accuracy indicates that 990 out of 1,000 characters are correct.

So, you can check the accuracy of an API by looking at the number of errors it introduces in an extracted text. 

Is There Any Ongoing Research Underway to Overcome These Constraints?

Since its inception, the accuracy of OCR technology has improved thanks to advances in computer vision and deep learning algorithms.

OCR tools can currently achieve 99 percent accuracy in extracting data from typewritten texts. However, higher degrees of accuracy are sought because organizations still use human interaction to check for potential errors.

Most OCR technology research focuses on character recognition and cursive text recognition.

What Is The Development of Handwriting Recognition?  

Handwriting recognition research takes advantage of the dynamic motion occurring during the writing process to recognize characters. 

While the variety of character styles is the fundamental issue with handwritten character recognition, OCR accuracy in this area is steadily improving. Currently, research is focusing on improving neural network algorithms, which can help increase handwriting recognition performance. 

What Does the Research Say About Cursive Text Recognition?

Connected characters are more challenging to recognize than printed text. This condition causes more problems in OCR technologies, as the letter shapes do not supply enough information for the software to interpret them accurately.

However, thanks to active research in this field, cursive text recognition has improved by leaps and bounds. For example, several cursive handwriting recognition engines provide accurate character representation. 

Filestack: Is It An Accurate OCR SDK?

If you’re searching for an OCR software kit that can provide you with higher than 99 percent data extraction accuracy, Filestack’s OCR SDK is the way to go. 

Filestack’s OCR SDK can help you recognize, extract, and organize data, allowing you to increase data extraction efficiency. It also assists in improving blurry images, extracting information from cursive-scripted documents, and creating high-quality extractions. 

Moreover, Filestack offers SDKs in multiple different languages and frameworks, so you can choose the one that best suits your needs.

Ready to Capture and Extract Data Using Filestack’s OCR SDK?

Filestack’s OCR SDK is versatile, accurate, and functional. It allows you to interpret, extract, and organize data without needing to lift a finger.

So, if you’re looking for an SDK that can meet all your data processing needs, sign up for free and start improving your business performance!

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