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The way computers can extract and interpret text from images and videos has completely revolutionized the management of documents. It's called optical character recognition (OCR), and it has improved the accuracy and efficiency of data management in industries such as finance, healthcare, and education.
Optical character recognition helps to convert written or printed text into machine-readable copies. It can be used for digitizing books or business documents, script recognition of handwritten texts, extracting text from a scanned image file for translation, and quickly performing search and analysis of digitized texts.
In this article, we'll look at the nature of OCR systems and the different techniques and tools it uses. Let's start with the basics.
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The terms "text recognition" and "optical character recognition" are often used interchangeably. Both of them refer to the methods used to locate and decipher text within a visual medium. In this post, we'll use the "optical character recognition" (OCR) term . OCR is the process of converting text from images automatically into a machine-readable text format, so that it can be modified, searched for, and stored digitally.
Popular uses of the text recognition technology include reading printed text aloud to the visually impaired, translating, processing text found in images, and converting printed documents into editable copies. Additionally, it can be used in machine learning and AI to categorize and extract useful information from massive amounts of text data.
Text recognition can automate manual tasks, increase productivity, and broaden access to information that was unavailable before, significantly improving the efficiency of work in sectors like business, education, healthcare, and government. And with the advancement of OCR technology, the potential applications of text recognition are only likely to expand.
So how does optical character recognition work? It relies on optical character recognition software that recognizes text in an image by "looking" at the shape, size, and font of the letters and then matching the image to a library of known characters.
Today, we can appreciate how much OCR technology has evolved since only a few years ago. OCR systems can now understand more fonts and font sizes, as well as handwriting and lower-quality images, though precision may vary depending on the nature and complexity of the input data.
Issues may arise when dealing with more complicated and unconventional fonts and layouts, as well as blurry, faded, or hard-to-read texts. To increase accuracy, the most promising strategy these days is to employ machine learning algorithms that have been trained on huge datasets.
Simple optical character recognition (OCR) systems rely on databases which contain characters and symbols in multiple fonts, which it uses as patterns. OCR software uses various algorithms to detect symbols in images and match these symbols to patterns in its database. However, this approach has its limitations, especially when it comes to uncommon fonts, low-quality images, or handwritten text.
Modern text recognition systems, also known as advanced methods of optical character recognition or intelligent character recognition, rely on machine learning and neural networks to gradually update their databases with new patterns. Such systems provide better accuracy, especially for uncommon fonts and handwritten texts.
Text recognition technology relies on optical character recognition algorithms to analyze images or scanned documents and identify the text contained within. These algorithms are able to recognize patterns in the images that correspond to different characters and words.
To convert detected elements into machine-readable text, OCR software maps the characters or words to the corresponding characters in its database, and then outputs the resulting text as a string of editable, searchable, and storable digital characters.
Let's take a closer, step-by-step look at this process.
The first step is quite obvious, but it should not be overlooked, as it might take a significant amount of time. To perform optical character recognition of an image, you need to transfer it into a digital format. For example, scan a book or a stack of legal documents.
To begin the text recognition process, the image must first be preprocessed to remove any irrelevant information, which both increases the image's quality and reliability and speeds up the processing time for subsequent steps. During this stage, the OCR system may crop the image to focus on the area with the text, fix its orientation or perspective, increase its contrast, and remove any noise or distortion.
During pre-processing OCR software prepares digital images for analysis. Common pre-processing techniques include:
If an OCR system utilizes machine learning, tt this step, it might also identify the text in each file using a combination of machine learning and image processing techniques such as edge detection and connected component analysis to segment the image into smaller pieces (also called "tiles"), individual characters, or words, and examines each tile individually to identify the text within it. Edge detection, for example, can be used to pinpoint the contours of individual letters or words, while connected component analysis can sort clusters of pixels as belonging to the same or different objects.
Once characters or words have been identified, the OCR software extracts features such as their shape, size, orientation, and the patterns of pixels within them in order to further classify each element. To differentiate between various types of letters and identify individual characters, the OCR software may, for example, take note of the width and shape of the strokes, as well as the location and orientation of any curves or intersections.
Optical recognition algorithms are typically divided into two types: matrix matching and feature extraction.
After the recognition and matching stages are over, there is still room for improvement, usually called post-processing.
There are several approaches to improving the accuracy of optical character recognition. One of them relies on a lexicon — a list of words which are expected (allowed) in the resulting text document. For example, a lexicon can be limited to only the English language, or to a specific field such as agriculture or medicine. The common problem with this approach is the presence of words not included in the lexicon, such as proper nouns.
Modern OCR software also improves accuracy by utilizing machine learning algorithms that have been trained on large datasets of text and images. OCR software compares the extracted features of each element to the features of known characters or words to determine the most likely match, thereby classifying each element as a particular character or word. Natural language processing (NLP) techniques may also be used at this point to analyze text structure and context, steps that are particularly important for document classification.
And finally, as a result of the whole text recognition process, OCR software converts the extracted text into a sequence of digital characters and stores it as a text file.
Let's take a look at some optical character recognition applications to see how it can be useful.
With OCR technology, a scanned document or a photo with text can be converted into digital text format in just a matter of clicks, making it easier to save, look for, and share data. Digital texts that have gone through OCR algorithms become available for editing in a word processor, indexable and easy to search within bigger document libraries.
OCR software allows users to make scanned documents more accessible by transforming them into digital text, which can be read by screen readers and other accessibility tools.
OCR technology lets users search for particular words or phrases inside a scanned paper document, making it easier to find and uncover information.
OCR software eliminates manual data entry by identifying data directly from document images, extracting information, and automatically entering it into a database or spreadsheet, thus saving time and scaling down errors in data processing.
OCR technologies can be utilized to automate how documents are sorted, sent, and stored, reducing potential mistakes, cutting down on space needed, and generally improving document management processes. By keeping all records in digital form, the need for maintaining physical duplicates can be eliminated, and it will be simpler to locate what is needed by sorting them based on the content, title, or even particular keywords.
The use of OCR software allows for the extraction of text from documents and its subsequent translation into another language.
Since digital copies can be password-protected and backed up in multiple locations, they are much more secure than paper ones.
To train character recognition models, researchers use OCR (optical character recognition algorithms) to sift through massive amounts of text-labeled image data to identify the pixel patterns that correspond to various letters and words.
Let's now go through the steps involved in training a character recognition model.
In order to train the model and evaluate its performance, you have to first collect a dataset of images which contain text. This can be a data annotation task of its own, when human annotators are to select whether the text is present on an image, and where exactly it is located. The dataset must be diverse and representative of the kinds of images and text the model is expected to encounter in the real world.
Before feeding the images into the model, they must be preprocessed to improve their quality and make them easier to analyze. This might involve cropping the images, enhancing the contrast, and removing any noise or distortion.
To provide a model with data to train with, this data must be labeled first. Assume that in the first step, we've collected a dataset of images with text. The next step is to transcribe these texts, so that the model can learn to detect and recognize pixel patterns as characters.
At this step, the model is fed with data and trained to recognize and decode characters and words.
Once the model has been trained, its accuracy and credibility must be verified by comparing its predictions with the actual labels in a quality control dataset.
Tweak and turn the parameters or add additional layers to the neural network until you get to the desired level of accuracy.
Text recognition is a process that allows us to process written words from a variety of sources, and it can be used to improve processes such as data entry, document management, language interpretation, and access for the visually impaired.
Developing an ML model for character recognition needs a dataset of labeled pictures and a machine learning algorithm. The accuracy of the text recognition system can be improved by altering the model's parameters, which involves training the model with a substantial amount of labeled images and then refining it on smaller sets.
Text recognition is a strong tool that has the capability to markedly simplify and streamline multiple duties and processes. It will keep on playing an essential role in the digital world as it changes and becomes even more advanced. There is no doubt that we will witness more companies taking advantage of optical character recognition and similar technologies.