We all have handwritten notes or hardcopy documents to convert into digital formats. They could be scribbles from a lecture we attended or a doctor’s letter written in hard-to-read handwriting. These documents will contain valuable information that will serve us better when typed out and viewable on a computer.
The Incredible World of Image-to-Text Processing
If you’re familiar with technology, you’ve probably heard of Optical Character Recognition (OCR). This application of machine learning converts a picture into text. An OCR tool is highly valuable because it recognizes handwritten documents, despite them containing natural languages.
Right now, the text you’re seeing on this screen appears in coded characters. They’re codes that your computer understands. Put simply, machine learning technology in OCR tools can train a computer to read and understand natural languages.
One cannot digitally edit handwritten notes. That ability only surfaces when we convert the original documents into a computer-readable format. For that to happen, the handwritten piece of paper has to go through a few phases.
Image Pre-Processing
Let’s say you have a doctor’s note in handwriting. You want to make it so everyone can read the document without misinterpreting its content. So first, you’ll take a photo of it. Before text extraction, the picture has to go through these steps:
· Binarization: Here’s where the conversion software processes the image to appear in two colors: black and white (usually). Thanks to higher contrast, the aim is to make character recognition easier.
· Deskewing: Most times, images of text appear skewed. This means that they’re slanted or not level. By deskewing the picture, the software rotates it so that it’s horizontal.
· Noise Removal: At this stage, the image is cleaned. The tool removes noise consisting of stray pixels, dust, and blots to produce a clearer picture. All these elements confuse text extraction by distorting the shapes of characters.
· Line Removal: Some documents contain lines, whether along the margins, to mark table columns and rows, and so on. All these lines can also change the shapes of characters. Therefore, the tool will remove them.
· Zoning: An image may consist of parts with text and those without. Zoning is where the OCR software draws boundaries to mark the former.
Image Processing
After the document goes through the initial prep process, it is ready for the core of image-to-text conversion. Here’s a closer look at what happens then:
· Tokenization: In this first stage of image processing, the OCR tool recognizes the boundaries of each character, marking the separate tokens.
· Character Recognition: After tokenization, the software uses one of two methods to identify the characters in order before forming them into words.
· Pattern Recognition: The tool compares the tokens against a library of symbols (natural language characters) for this approach. In the case of a match, it extracts the symbol. For non-exact comparisons, the software grabs the closest lookalike. Pattern recognition is effective when image text sizes match the database symbols.
· Feature Recognition: This technique applies certain rules to identify characters. For example, the letter “A” consists of three lines, two at an angle and one bridging them. The process can be cumbersome. However, it suits documents with various fonts and handwriting styles.
Post Processing
In this final stage, the tool touches up the results. It ensures that the extracted words exist in the dictionary. Otherwise, the tool will pull the closes match.
However, the latest OCR tools utilize machine learning technology to improve the outcome of this step. Artificial intelligence will aid the software in recognizing the way people form sentences. It may also enhance the use of the software as time goes by. The OCR tool does so by getting used to a person’s handwriting and understanding the nuances between particular letters and numbers.
Are Humans Still the Best at Image-to-Text Conversions?
Technology has certainly come a long way in this regard. One only needs to look at OCR results from 20 years ago versus today. Decoding imagery makes data entry a much easier process. Take the job of secretaries, for example. Before, they had to hand record all their manager’s receipts to calculate expenses. Today, their task simply consists of snapping photos of the bills and letting OCR technology work its magic.
However, despite the improvements we enjoy, OCR may still fall short compared to professional transcribers. Software may return an accuracy rate of up to 90%. The experts at GoTranscript, on the other hand, will produce astounding near-perfect results.
You may recognize that time is a factor here. OCR converts in seconds, while humans may take 15 minutes for a single page. As a client, you must ask yourself how much you will sacrifice. In sensitive industries such as the medical and legal fields, it’s pretty obvious. A flawed result may lead to disastrous effects. The choice is yours.
