Hello dear reader, hope you’re doing super well, whatever time of the day it is for you. In the following post we will be speaking about Machine Learning OCR, a topic we love, and that now with all the LLM Multimodality thing is evolving a lot.
In this post we will be covering the basics, so lets get to it!
Introduction
Machine Learning OCR is an Optical Character Recognition technology embedded with machine learning algorithms. It is used to identify and extract text from pictures and scanned documents.
Humans, being intelligent, can easily recognize the information in the images. But it is very difficult for a simpler tool.
Simple OCR can only recognize the set of data that is fed into it. It stores fonts and images of texts to match with the given data. But it has many limitations. So, to resolve this issue, machine learning OCR has been developed. But what is this? Everything about it is explained below:
Everything About Machine Learning OCR
In this section, we are going to discuss every possible detail about machine learning in Optical Character Recognition.
- Difference Between Simple OCR and Machine Learning OCR
Simple OCR is capable of reading text that matches its stored templates, such as characters. Its accuracy is low and requires human intervention.
While machine learning OCR is embedded with machine learning algorithms and can be trained to recognize the text that a simple one can’t. It requires less or no human intervention. Its accuracy is higher than the simple one.
It has datasets of human languages so it can auto-detect a character even if it was blurred or missed in the image.
- Drawbacks of Simple OCR
It is a basic version of its kind. It needs high human intervention to recognize the text in unstructured data. It is unable to perform even average in the unstructured data. When it encounters semi-structured data, it performs a bit better.
However, it requires human intervention for accuracy. In the structured data, the performance and accuracy are good. But even then, it is not 100% accurate and requires human help.
It has many other drawbacks. Such as, when it encounters unique fonts or complex fonts, it fails to recognize them. If the text in the image is handwritten, it faces much difficulty in understanding it. Because it doesn’t exactly match the characters fed as a reference in it.
If the text is written in style/ligatures, it is unable to identify the joint characters. Because it is unable to create segmentation between characters.
If the source document in the picture has flaws or wrinkles in it, it is unable to identify the text. And if the font is faded, or not typed perfectly, it also gives it a tough time.
If a picture has some graphics that contain text or the font is written on an image, it fails to recognize it. And if the picture is blurry, even then it is unable to extract text.
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Advantages of Machine Learning OCR over Simple OCR
It has machine learning algorithms. These algorithms don’t only help recognize the text but add context to it. This way, it becomes able to identify the adjacent unclear characters.
It can understand and extract unstructured data, which is impossible in the case of the simple one. We can explain it with an example of how this algorithm works.
If we give data to the machine learning algorithm, (5, e) (6, f) (7, g) (8, h) (9, ), it gives us the output “i”. It added context to the given input and related the alphabet with a number, which enabled it to answer “i” for the digit 9. This cannot be expected from the simple one.
However, it is important to note that, the algorithms of machine learning OCR only work when it is paired with some kind of tools.
The machine learning models used in an advanced image to text converter enable it to remove any distortions, noises, etc. from the pictured document or image to recognize the text. The tool then adjusts the alignment of the document if it is tilted to an angle which makes it difficult to identify the text.
In case of poor alignment, the ML-based image-to-text tools will automatically adjust it and extract all the text in an editable format without compromising on accuracy.
Drawbacks of Machine Learning OCR
It has many advantages over a simple one but it is not perfect. It needs a lot of improvements yet. Some of the scenarios where they are unable to operate accurately are discussed here.
- It needs diverse training, otherwise will lead to inefficient results.
- When the data has some variations, such as changes in placement of dates, contact details, etc. They give it a tough time.
- In handwritten documents, when fonts, colors, and styles change, they also make it inefficient.
- It also struggles to understand the context of the text and sometimes leads to mistakes.
- Unique characters and symbols require additional training, otherwise it will not recognize them.
Final Words
Machine learning OCR can be trained to perform a lot better than a simple one. It can understand the context of the text, realign the document, and identify the missing characters. But it has many disadvantages too.
As it is unable to understand handwriting of varying styles. It requires diverse training to perform well. Otherwise, its model will fail to work with new formats, styles, and symbols.
As always, thank you so much for reading How to Learn Machine Learning, have a wonderful day and keep it up!
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