OCR is short for optical character recognition. It is known as a technology that recognizes text. It certainly requires a script or software that extracts the recognized data for repurposing.
So, when does it recognize characters?
Certainly, these are the text in a scanned document, camera images, or PDFs (image files). The script or software separates characters on the image and collect them into words, and then, forms sentences. This is how the content is extracted from the original image files, saving many hours of manual data entry.
The OCR technology needs dual support, which is of hardware and software to convert images into machine-readable text. Remember that scanner is a hardware that copies or reads text. Then, the script or software executes the next step of advanced processing.
At present, OCR software leverage artificial intelligence (AI) to carry out next-level methods such as intelligent character recognition (ICR). With it, identifying languages or handwriting becomes easier. Simply put, this is actually a document conversion technology that transforms a hard copy into a PDF, and then, a digitized file. This file can be edited, formatted, and searched in no time.
How Do You Work with OCR?
A piece of information that is aforesaid, explains it all. This technology uses a scanner to capture the image of a document. Once all images are scanned, optical character recognition software starts processing. It separates inked characters from the white background. The scanned image is thoroughly examined to split light and dark characters.
The dark characters are further recognized from the stored information. And, the white space is treated as a background. Furthermore, the inked or dark characters are processed as alphabet, words, or blocks of sentences at a time. Eventually, algorithms are used to recognize patterns or features.
Pattern recognition is a part of AI modeling, which is mainly used to filter such patterns of text that are already used (although in different fonts and formats). It guides you to compare and identify characters (as what they mean as a word or sentence) in the scanned file (which can be a PDF).
Another significant thing is feature detection, which takes place when the OCR technology uses protocols related to the features of a specific letter or number or character in the scanned copy. In all, features cover angled lines, crossed lines, or curves in a character. For instance, the letter “C” has a curve line. So, it recognizes it as a curve (feature), which is translated into an ASCII code (that stands for American Standard Code for Information Interchange). The computer software uses it to control further changes.
Once determined, the analysis comes into play. This technology also assesses the structure of the file or image document. For this, the entire page is split into various elements like blocks of text, tables, or images. Then, the lines are further categorized into words, and finally, the characters. As this process is complete, the software starts comparing them with the already fed pattern images in its own memory. This is how the characters are matched. The program identifies them as the recognized text.
Why Should You Consider AI-based OCR?
This blog gives 3 clear reasons why the choice for a traditional OCR player might have made sense 20 years ago, but today is unjustifiable compared to an intelligent document processing alternative.
The reasons are obvious. AI-powered OCR is more self-reliant and optimal to use. Besides, there are three main reasons that support modern optical character recognition technology.
There are different ways for measuring accuracy within a document processing project. The very first step is to convert images into text. Here, you can find out the characters that are correctly read and converted. Determine, let’s say, 0 and o if they are accurately converted. Typical OCR systems found it difficult to distinguish them.
On the flip side, modern technology has minimized such errors. However, manuscripts or (cursive) handwritten documents are still an exception because of their low readability. Although technology innovations and evolutions have proved that traditional document conversion methods are lower in terms of accuracy. Unfortunately, some very bad cases are still the case of limitations.
- Legacy Software
OCR software has been around for over a decade. Many companies have been using it for years. Even, they are aware of its downsides. They know it’s not an optimal method of conversion. But, they won’t have any other option that can be economical and easier. As a semi-optimal choice, they have been using legacy software.
The tragedy with its old version is that it’s old-fashion and users don’t find it comfortable to work with. The new, Intelligent Document Processing SaaS platforms are equipped with the latest technologies. Users like to work on it because of its simpler and better architecture.
Moreover, it can be integrated with different departments. On the flip side, traditional systems were no less than a nightmare to configure and integrate with.
- No Self-Learning Model
Traditional OCR systems were unable to self-learn by drawing users’ feedback. Many enterprises and organizations are still dependent on these systems. They have to create and maintain templates and also, pay massive consulting fees to Optical Character Recognition services provider or software vendor. This situation arises when the structure of your documents gets changed. Certainly, the traditional system won’t be able to interpret it and there, it fails.
On the contrary, more OCR software is developed that ensures self-learning from user feedback. It uses reviews or feedback from users and draws insights from them. This happening ensures the retraining of models inside automatically. These all things take place behind the scene. Typical systems were not able to do so. You always need the vendor or service provider to make changes as per changed templates and fix its conversion capabilities. And sometimes, the corrections won’t happen even if they spend hours on validations.
The modern and advanced applications are able to self-learn and fix issues if there is any change appears in the template. This is a smart move that only AI-powered modern optical character recognition systems can make because they have AI components. It helps in retraining the models and checking accuracy. Finally, the customer wins the ability to manage his own models, which increases their efficiency and performance.
This conversion innovation is actually evolved to simplify data entry processes. Its introduction makes this process effortless. It provides a speedy digitization flow, which ensures quick searches, editing, and online storage. If you consider the storage capacity, it is amazing because you won’t have to settle down with limited storage space. You can have a server to save colossal data.
With these advantages, you also win some more ones that are given below:
- Cuts cost on conversion
- Accelerate the flow of work
- Enable automation process, which encourages quick document routing and content processing
- Centralize and secure the database
- Improve interactions between departments and employees via up-to-date and accurate info in motion.
Optical character recognition or OCR technology ensures document conversion within a few seconds using machine intelligence, scripts/codes, or software. This technology requires AI support for being able to learn by itself if there are any changes made in the source documents.