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Optical Character Recognition is the technology used to convert images of handwritten or printed text into a digital machine-readable text, this way the text can be edited, manipulated, or processed electronically. Through its system, OCR software allows you to extract written data from a scanned document or image without having to manually enter it.
With this technology, you can turn the text in a PDF or even a phone-taken picture into an editable document in just a couple of seconds or maybe minutes, depending on the size of your document.
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.
Recently AI has become more and more popular and AI applications more prevalent. So how would AI work with OCR? There are several potential applications of AI with OCR software. It could be used to enhance or potentially even replace traditional OCR software and use machine learning algorithms to extract and convert the document into editable text.
A second application would be as an add-on to OCR software, OCR can be used to read the file and convert it into an electronic document. AI can then be used to read and analyze the document. This could be helpful if you want to automatically classify the document for example or extract a certain part of text. Additionally, AI could be used to check the electronic document and flag and potentially even attempt to fix mistakes that occurred during the transition to a digital version.
NLP or natural language processing is a method through which machine learning is utilized to try and give a computer the ability to understand, interpret, and comprehend language. This is very useful since OCR requires the characters to be matched to words and vocabulary, but the next step in that process would be for OCR software to understand the meaning of language.
If that would be the case then the software can make decisions on what is correct and what is not correct and needs to be corrected or omitted. This is especially interesting in the case of lower accuracy OCR or handwritten texts since it can fix grammar mistakes and still output a comprehensive text without potentially knowing all the characters.
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.
Standard OCR software needs bitonal images, this means that each pixel in the image file will be interpreted as either black or white and that tonal value is then stored in one bit of digital data). This data is then used to interpret what character it is. Jetstream AI’s abilities on the other hand go beyond the need of bitonal images. Jetstream AI uses deep learning technologies that are baked on neural nets and GPT.
By using AI, Jetstream offers 99%+ accuracy for machine-printed text and over 95% accuracy for handwritten text. Since it is an AI solution it can also learn and adapt over time to the specific needs of the business or industry it is being used in. Jetstream AI has also been trained to better combat the current OCR limitations like interpreting glary, highlighted, or smudged text.
The Jetstream software also offers the ability to search inside of documents, so instead of having to convert a document, which can cause errors or mistakes, and then search it. With Jetstream you can directly search in documents with handwritten text and find instances of that word or phrase. Additionally, it can be easily incorporated into automation workflows to for example classify the documents or extract certain data based on a set of parameters.
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.
We are excited to answer any questions and can provide virtual demonstrations, document testing and free trials.