Character recognition in image processing

Introduction

As the world moves toward rapid digitalization, there’s an ever-growing need for documents and data to be converted into online formats. Not only does this help with office space and storage, but digital documents are also more secure. (Optical character recognition)[https://toloka.ai/image-data/] (OCR) is a key part of document digitization and a valuable technology that’s being swiftly adopted by companies across industries today. Due to its automated data extraction and storage capabilities, OCR can save you time, money, and resources.

From letters written by hand to printed papers, you can learn how to use OCR to convert any type of image-based document that comprises written copy into machine-readable text data. We’ve outlined all you need to know about this technology: how it works, what the key benefits are, and how you can use crowdsourcing to train and fine-tune a machine learning model by incorporating human input.

OCR: What is it and how does it work?

Let’s start off by defining what (optical character recognition)[https://toloka.ai/image-data/] is. Occasionally mentioned as text recognition, OCR involves turning an image that contains text into a format that a machine can read. OCR applications mine data from documents, photos, and image-only PDFs. An example of this is when you scan a document and the computer saves the scan as an image file. In short, OCR helps or aids in the process of changing that image file into a text document, the content of which is then stored as text data. We’ll cover document text extraction and text recognition, which both fall under the umbrella of optical character recognition.

What’s beneficial about optical character recognition is that it can extract and reuse data from scans, images, and image-only PDFs (as mentioned above). The possibilities are infinite: for example, large volumes of historic or legal documents can now be converted into PDFs that people can edit, format, or search for specific information in, just like in an editable document. In fact, OCR is mostly used to acquire information from printed publications and manage different types of paperwork, including many of the materials found in a typical office setting, from printed forms and invoices, to contracts, proposals, transaction records, and more. While most companies have gone paperless, there are still some drawbacks to having all these documents as scanned images, since word processing programs can’t process text in images. That’s where optical character recognition comes in handy: it changes the image into text data, which has a wide variety of uses, such as performance and operations management, as well as data analytics and process automation. You can see how convenient this would be in the day-to-day workings of so many businesses and the everyday life of so many people.

So how does it do this? Optical character recognition technology can identify letters in an image and arrange them into words. It can then form these words into sentences. You don’t need to enter data manually and it even lets you access and edit the original material.

As mentioned, optical character recognition systems can transform actual paper documents into machine-readable text. An optical scanner or customized circuit board makes up the hardware part of a system that copies or reads the text. The software part takes care of any advanced processing and leverages AI to incorporate more sophisticated intelligent character recognition (ICR) techniques, such as handwriting style or language recognition.

Traditional versus deep learning approaches to OCR

Generally, (optical character recognition)[https://toloka.ai/image-data/] approaches are founded on either traditional image processing (based on machine learning), or deep learning techniques. The former involves multiple pre-processing steps where the document is cleaned up and de-noised, followed by contour detection to identify lines and columns via character extraction, identification, and segmentation through multiple machine learning algorithms. This technology involves several steps:

1. Image acquisition

OCR software analyzes scanned images and distinguishes the light from dark areas as background versus text. This is necessary to uncover alphabetical or numerical digits by processing the dark sections.

2. Preliminary processing

Next, the OCR software cleans the image by straightening and smoothing out the document, thereby fixing any contrast issues and ridding it of any other defects.

3. Text recognition

OCR software implements pattern matching and feature extraction to recognize text, generally focusing on a single character, word, or section of text at a time.

4. Pattern recognition (pattern matching)

Pattern matching involves drawing out an image of a character (known as a “glyph”) and comparing it to a similar character, but this will only work if the font and scale are the same. By feeding the OCR application with samples of text in different formats and fonts, you can use pattern recognition to evaluate characters.

5. Feature recognition (feature extraction)

This algorithm breaks down glyphs into different geometric features like lines and their directions and intersections, and uses them to determine the best match among stored glyphs. The OCR application applies rules regarding the characteristics (such as curved, crossed, or angled lines) of a certain letter or number to identify characters in the scanned document.

6. Finishing

The OCR software breaks a page down by section including text blocks, tables, and graphics. Words are distinguished from lines to form characters and the algorithm then compares them to a collection of pattern images. The text data that’s been extracted is then turned into a editable file.

Image binarization, style classification, and refinement are also steps of the process. A binary image can be helpful in terms of reducing the amount of time required to extract a section of an image. Likewise, font identification can help determine if the text is written by hand, and by whom. You can further improve an OCR’s output by using a glossary to transform illogical words into their closest related versions that are likely to be correct.

Although traditional methods are easier to build, they’re more time consuming and can be quickly outpaced by deep learning approaches. Traditional approaches are excellent for printed and handwritten materials, but they’re not as effective when it comes to more complex datasets.

By contrast, OCR methods that involve deep learning can extract a multitude of features simultaneously. Algorithms that are based on both computer vision and NLP have proven to be highly successful in producing results. Plus, they don’t have to follow the same pre-processing steps that traditional methods do.

Furthermore, these deep learning OCR methods can extract text-based sections and predict bounding box coordinates. These are then passed onto language processing algorithms that use RNNs, LSTMs, and transformers to decipher and convert the information from the features into text data.

Unlike traditional methods, deep learning OCR algorithms follow two main steps:

1. Regional proposal

This step involves detecting areas of text within an image. Convolutional models detect parts of text and encircle them within bounding boxes. This is similar to object detection where certain regions are marked, withdrawn, and leveraged as attention maps. Then they’re fed to language processing algorithms along with the features that have been pulled out of the image.

2. Language processing

In this second stage, NLP networks extract the data in these regions and create cohesive sentences from the features fed from Convolutional Neural Network (CNN) layers. As a side note, a lot of research has been done on CNN algorithms that can identify characters directly on practical use cases such as detecting text that has minimal temporal information, such as automobile registration plates.

Moreover, deep learning helps to alleviate poor quality scans. Google Earth leveraging OCR to identify street addresses or paper documents being digitized and mined for data are a couple relevant examples of how deep learning is applied to unstructured text scanning.

Ways to improve current OCR methods for image recognition

Text detection algorithms are key to OCR methods used today, and neural networks are adept at identifying text within documents and images at all angles. While current OCR methods have a number of impressive capabilities, there’s always room for improvement.

You can achieve greater OCR accuracy via the following approaches:

1. Denoise input data

In order to preclude non-text regions from being suggested as text, data fed to the model should be fully denoised. Gaussian blurring is one of the best methods for this. Additionally, white noise can also be eliminated with an autoencoder.

2. Enhanced image contrast

Contrast in images is integral to aiding neural networks in distinguishing sections of text from background areas. Boosting the contrast between the text and the background improves the performance of optical character recognition models.

What kinds of OCR technologies exist for scanned documents?

There are several different types of OCR systems that are categorized based on how they’re used. For example, Intelligent Character Recognition (ICR) and Intelligent Word Recognition (IWR) are more sophisticated subgroups of general OCR systems. These subgroups focus on handwritten rather than printed text.

Here are some of the main types:

Simple Optical Character Recognition Programs

These programs leverage stored font patterns and text-based images as templates. With the help of pattern matching algorithms, the OCR software compares images of text glyph by glyph with those stored in an internal database. This forms the basis of optical word recognition, where text is then matched word by word. Still, there are limitations as it’s impossible for the database to account for and store all the various fonts and handwriting styles that exist.

Intelligent Character Recognition Programs

Modern OCR systems implement human reading skills and strategies through intelligent character recognition. The neural network analyzes text via curves, lines, intersections, and loops through different levels by repeatedly processing images. The ICR-processed results are provided within seconds.

Furthermore, ICR software can divide symbols into the elements listed above in order to detect individual handwritten characters (not cursive). Moreover, ICR tools identify highly structured characters that are evenly arranged. For example, those found in a questionnaire, such as an exam or a medical survey, where someone fills out answers in the respective blank fields or boxes.

Intelligent Word Recognition

Likewise, intelligent word recognition utilizes the same principles as ICR; however, IWR views entire words without extracting the characters found in an image. IWR is applied to unstructured, freehand, or cursive handwriting. The goal is to identify the whole word rather than separate characters. Free form handwritten notes are a good example of where IWR is used: entire words or phrases are identified.

Recognizing and decoding handwritten text can be difficult, but IWR aims to address this challenge. With IWR, there are significantly fewer mistakes because it matches handwritten words to a user-defined glossary.

Many of today's applications combine all three approaches: IWR, ICR, and OCR. With the help of this technology, you will be able to easily note logos, watermarks, and other designations in documents

Advantages of OCR and image processing for text recognition

As previously alluded to, OCR technology can be utilized differently based on specific data scientists need, its use, and application. The main benefit is that it can simplify the process of data entry via search, editing, and storage capabilities. With the help of OCR, individuals and businesses alike can store and access files on their devices that otherwise would take up a ton of office space. There’s no doubt that businesses will continue their rapid adoption of this technology for a variety of reasons.

The key benefits of using OCR technology are outlined below:

Improved accessibility and searchability

OCR-scanned documents are simple to index by their content, title, or key words, which makes them easy to find in a larger database.

Greater productivity — and no more manual data entry

OCR software makes life easier, automating manual workflows by scanning, reviewing, and analyzing paper documents, as well as performing a quick search of the database and transforming handwritten notes into editable texts. OCR can rapidly identify data directly from document images, thereby ridding companies of the need for manual data entry. There are also fewer errors this way.

More AI-based solutions

OCR plays a key role in many AI solutions, such as identifying road signs for autonomous vehicles, or recognizing brand logos or product packaging in social media posts or advertisements. A key benefit to these AI solutions is that they help companies make better decisions at a faster rate and at a lower cost, which culminates in a better customer experience.

More storage space

By turning paper documents into digital copies, OCR can help your company increase storage space. Documents stored in text form are significantly smaller than those stored as paper copies or as images.

Other advantages of OCR include lower costs, improved manual workflows, automated routing of documents and content processing, greater data security, and better overall service.

Uses and applications of OCR

The most well-known OCR use case involves optimizing big data modeling by transforming paper and scanned image documents into searchable PDFs that can be read by a machine. In other words, it involves being able to digitally edit a scanned paper document that has gone through OCR processing. Think how easy it would make things to be able to edit an old legal document in Word or Google Docs.

With such tremendous potential for efficiency, searchability, and improved workflows, it’s no wonder that many industries are looking to incorporate OCR programs into their businesses.

Here are just a few examples of how OCR technology is being used:

Banking

Banks, professional services firms, and other financial institutions are using OCR in their day-to-day transactions to process and digitize millions of client documents per year. Not only does OCR make tasks like depositing checks or moving money more efficient, it also helps safeguard against fraud and allows for faster storage and retrieval of vital information.

Healthcare

Processing patient tests, insurance payments, and hospital records are just some of the ways in which OCR is helping to streamline the healthcare industry.

Logistics

Companies that focus heavily on logistics such as mail or transportation services apply OCR technology to their workflows to help track packages, invoices, receipts, and more.

Document identification

Text detected via OCR can be used to categorize documents into groups, which makes it about a hundred times easier to access them.

Data entry automation

Given that data can be effectively retrieved from documents with the help of OCR, manual data entry has been relegated to the past. It’s also faster, less expensive, and more accurate.

Digital libraries and archives

OCR can help classify volumes of works by class or genre, as well as digitize archives. Essentially, OCR makes it easier to look up books in different categories and preserve old documents.

Text translation

Particularly relevant to text recognition, text translation can help visitors in foreign countries understand signs, street markers, and billboards in different languages. Translation modules are added onto OCR system output to achieve this.

Sheet music recognition

OCR systems can be trained to identify musical notes, whereby a machine can learn to play music right from the text data. Imagine your music teacher being a machine!

Marketing campaigns

OCR systems can also be used in marketing campaigns for fast-moving consumer goods. This is done by appending a scannable section of text onto a product, which can then be transformed into a text-based code to redeem promo codes.

As you can see, this amazing technology powers many aspects of daily life. For example, it can be used to index personal documents such as passports, bank statements, business cards, and more, or it can be used to help those with visual impairments.

With the help of computer vision, OCR detects and reads text in images, which allows Natural Language Processing algorithms to decode the text, convey the meaning, and even translate it into different languages. Incredible advances have been made in this technology. Not only can OCR detect text from images, but it can also identify, translate, and interpret product names, road signs, and billboards.

How crowdsourcing can help

Even though OCR technology has progressed by leaps and bounds, it’s still helpful to have humans involved in the process. Crowdsourcing can be a highly effective way to help you digitize your paper files. Decomposition of tasks and robust functionalityincorporated in Toloka, can save you time, energy, and hassle.

1. Break the task down into manageable pieces and classify content

If you have a set of documents that you’re working with, you’ll first need to decompose the content into different categories that aren’t text: drawings, musical notes, or others. Categorize all your documents into text and non-text components to get a head start on your pipeline. Toloka can help you achieve this through channels such as image classification.

2. Allocate the content into different sections

Then, divide your documents into smaller sections. Longer tasks such as decoding handwriting are more fatigue inducing, so you’ll want to help out your crowd contributors as much as possible here.

3. Break images down into smaller pieces

Divide the images you have into smaller segments by outlining paragraphs. Use Toloka’s “Selecting a region in an image” template for the best results.

4. Verify segmentation

Here you need to ensure that your images that contain text have been properly segmented. You can launch a simple binary classification project and ask Toloka’s annotators to check each other’s work.

5. Transcribe text from images

Now you can transcribe text from your images. Apply our “Text recognition from an image” template to get started.

6. Incorporate quality controls

Enact restrictions on quick responses, introduce control tasks, and assess the overlap of Tolokers’ answers to make sure they’re all on the same page.

With the help of our crowdsourcing platform, here’s what your pipeline should resemble:

Key takeaways

To sum up, OCR uses a scanner to process paper documents, which it then converts into a black-and-white version where light and dark areas are deciphered from one another as background and text, respectively. Letters and numbers are extracted from the dark areas where a single character, word, or section of text is targeted in succession. At this stage, either pattern or feature recognition is used to identify characters.

Remember, pattern image recognition occurs when different fonts or formats are involved in the examples fed to an OCR program, whereas feature detection, which comprises angled or crossed lines or curves in a character, is used when the features of a letter or number are analyzed to identify characters in a document. In addition, an OCR program looks at the structure of a document and divides it into sections. Lines are separated into words and subsequently characters, which are then compared with a set of pattern images. After all that, voila! You get the recognized text.

Thanks to OCR text recognition technology, scanned documents can now be incorporated into big data systems that can read any printed document from bank statements and legal contracts to license plates and health records. Instead of agonizing over innumerable image documents, companies can now use their time more effectively thanks to OCR software, which can extract all the necessary data for them.

OCR plays an integral part in many companies’ journeys toward digital transformation by helping securely store their data and recover information with greater ease. For example, marketing firms make use of OCR algorithms to improve retention rates and increase sales via a streamlined customer experience.

Outside of the world of business, OCR also lowers environmental impact by reducing the number of hard copies and saving paper. It likewise improves access to information and bridging language gaps by helping translate written text into different languages.

If you’d like to learn more about how Toloka can help you with your next project, we invite you to browse through our blog for more applicable tips, tricks, and insights.