The software follows a stepped process to be able to recognize the characters. 

Starting with pre-processing: here the software basically “cleans” the document of non-glyph (a character image) elements and any other spot in the document. Also, it smoothes the edges, “tides up” any details like proper alignment of the text, and separates the background from the text. 

Later, the software’s algorithm starts recognizing the text through one of two methods: pattern recognition or feature extraction. The first takes each image or glyph (the letters or characters) as a whole and compares it with glyphs already stored in the software. Normally this method works better with neat fonts that can be easily recognized. The second method analyzes the parts of the glyph like its curves, lines, angles, and everything separately, to identify the letter. This method works with any kind of font, printed or handwritten, cursive, or script. 

Lastly comes the post-processing. This corrects any errors there might be in the detected text by comparing the words with the vocabulary stored in the software, which could be normal vocabulary or more technical/specialized in a specific field. 

Afterward, the text is ready to use. Some OCR software includes features like the creation of a file with the format of your choice, like for instance a PDF document, with the extracted text. 

The OCR technology can be applied both through hardware and software or software only. The hardware part mainly plays the role of a scanner and the software processes the information. 

A lot of this article has talked about OCR from a business perspective and for a business use case. However, OCR could also be used by consumers, for example by consumers taking pictures of something which they can then get translated or read aloud for them. Additionally, it could even help businesses, for example when an employee travels and has to upload bills for approval, a picture can be taken, the data can be extracted and enrolled into logic workflows in an automatic approval process.

One of the main limitations of OCR is that it will most likely require additional software or machine learning algorithms to provide the desired output. The basics of OCR try to extract a character, the result is a string of unrelated characters. As also mentioned above, vocabulary is then needed to form meaningful words and sentences instead of just characters. How successful that is depends on how good the vocabulary software is.

Accuracy is also still a potential issue with a lot of OCR software, for printed text the accuracy can be around 98-99%, which means that in a 1000-character document, 10-20 characters might not be correct. This limitation can be partially solved by using better vocabulary software and potentially machine learning, to be able to still output correct words and sentences. However, this does not include handwritten texts which could lower the accuracy by a substantial amount depending on the handwriting.



If the document is printed in a lower quality this could severely impair OCR capabilities. This includes when the text can get blurry, the text is smudged or the ink is of insufficient quality or quantity when printing. The DPI selected while printing can also have a significant impact, depending on the letter size, color, and how close together the text is, if the DPI of the page is low it could negatively impact OCR.

If a document is scanned or a picture is taken the quality of said scan can influence how easy it is for OCR software to extract characters. For example, if the image of the scan gets glary, this can make it difficult to read. Also here DPI can affect the quality, depending on what DPI is chosen for the scan quality. Lastly, if the document is not aligned properly or potentially skewed, character identification can get more tricky.

There is a large variety of letters from different alphabets, and certain alphabets are easier to recognize than others. For example in Arabic even printed letters could be printed in cursive which could make it harder to recognize for OCR, this could also include cursive with any alphabet. Additionally, there are many different variations of fonts and font sizes, with some drastically changing how the characters look. Couples, fonts, sizes, and layouts with certain letters or even numbers that look a lot like each other, like the zero and the letter o, and this can cause problems for OCR technology.

It is clear that OCR can already have problems with scanned computer-generated texts, due to several factors that can make it difficult to detect. In the case of handwriting, these factors are multiplied, since there are almost exponential differences in how someone might write. Depending on what alphabet they use, how they were taught, and more. Additionally, human-written texts are of course a lot more error-prone than computer texts that have spell or grammar checks. This can add an extra problem to OCR at the stage where the characters are trying to be matched to vocabulary.

Optical Character Resolution or OCR, is a handy tool that can be used to turn photos or scans into a regular text document that can be interacted with, searched, manipulated, or edited. It does this by recognizing characters in the document and then converting those characters to words and vocabulary. 

However there are currently still a lot of limitations with OCR technology, mainly accuracy problems, these accuracy problems have a wide range of causes from, print quality, scan quality, photo quality, text quality, different fonts, layouts, and more. Handwritten text is more prone to these limitations. There are also limitations with the interpretation of characters into actual words and sentences, especially if the accuracy of the interpretation is already low. 

Artificial intelligence, natural language learning, and machine learning seem to be the main factors that can help OCR technology. Especially on the identification of words and meaning from the characters. If the interpretation of the characters can understand the meaning of the language through AI it can correct mistakes that could have occurred during the OCR scanning. 

Jetstream AI solves many of these issues that OCR technology has, by using AI to overcome a lot of text quality issues to improve the accuracy of the OCR. Additionally, it allows for many more capabilities like training the model for a specific use case and searching handwritten documents for specific keywords or phrases directly in the handwritten document.