FAQ on OCR and machine learning
All you need to know in a few words
How do Optical Character Recognition (OCR) and Machine Learning work together?
Over the past few decades, Optical Character Recognition, also known as OCR, has become a popular phenomenon. It has widespread use in various fields where it digitizes printed text so you can use it in different machine processes after electronically editing, searching, and storing data.
The working of OCR has undergone several changes, and as of now, Optical Character Recognition uses Machine learning and Computer Vision to operate.
In this article, we discuss the working of OCR and its relationship with Machine Learning (ML), specially because this is at the center of our Virtualia Shop experience.
What is Optical Character Recognition (OCR)?
Optical Character Recognition, or OCR, is a procedure for analyzing and detecting handwritten/printed text images. Afterward, the text is converted into machine-encoded text electronically and mechanically.
Benefits of OCR
- With OCR, the conversion of physical text into digital happens with great convenience without any errors.
- Once OCR converts the hard copy into a digital one, the text can be easily edited and formatted. It eliminates the need for physical storage.
- Furthermore, the OCR-created digital documents help simplify navigating information in heaps of printed, handwritten, or typed documents.
- It is good that machines can do this overwhelming job because there is no chance of skipping through information.
- Coupled with automation, this can offer massive scalability.
How does OCR work?
Here is the working of Machine Learning-based Optical Character Recognition (OCR) technology:
a. Image pre-processing
In the first step, an optical scanner processes the physical form of the document in an image. This step removes any unwanted distortions and converts the image into a black and white image. The background is characterized by white color while the characters are black. The system also segregates different elements like tables, text, and imagery.
b. Intelligent character recognition
In the next step, with the help of ML, the dark areas are analyzed. It targets one character word at a time by using the following procedures:
- Pattern recognition
The Machine Learning algorithm examines the format and handwriting on the image to detect any resemblance with the already learned data.
- Feature recognition
However, in the case of new characters, the algorithm applies specific rules to examine them and then deduce results.
Once the ML algorithms analyze and recognize the characters, they are transformed into an ASCII code that allows further analysis.
c. Post-processing
In the last step, the AI fixes the errors in the final document. For this purpose, a lexicon is created. This post-processing phase can include further automation process.
We also include image recognition and classification to help the AI decide what field to fill in automatically.
How Machine learning-based Optical Character Recognition (OCR) is a game-changer?
As the enterprise industry expands and thrives, the volume of documents/data to be filtered has also increased. In addition, there will be many unstructured documents with various formats.
That is why template-based OCR cannot keep up with this, and we need an alternative. Here ML-based OCR steps in.
It precisely extracts relevant data from documents of unstructured formats.
With Machine learning, we can now automate this data extraction, efficiently analyze the data, and make automated decisions.
Applications of OCR
Optical Character Recognition has widespread uses. A few popular ones are:
1. Legal documentation
OCR converts important signed documents into an electronic version, making them accessible among multiple parties.
2. Banking
You can now scan the cheque you would like to deposit with OCR technology, and it will immediately analyze it to confirm the validity.
3. Word processing
With OCR, you can convert printed documents to a digital form, making them editable and searchable. ML and AI ensure the accuracy of such documents.
4. Virtualia Shop Update
With our OCR technology coupled with ML, AI, automation and 3D spatial analytics, store owners and staff can quickly update their backend database with just their phone.
References
https://appen.com/blog/optical-character-recognition/
https://parashift.io/en/why-machine-learning-based-ocr-beats-traditional-ocr-hands-down/
https://www.v7labs.com/blog/ocr-guide
Most common questions on OCR and machine learning
What is OCR?
OCR (Optical Character Recognition) is the technology that enables a computer to extract text from an image or a scanned document and convert it into machine-readable text.
How does OCR work?
OCR works by analyzing the visual features of an image or scanned document and matching it with known characters to determine the correct text. The process involves several steps, including pre-processing, segmentation, feature extraction, and recognition.
What is the difference between OCR and ICR?
OCR is used to recognize printed text, while ICR (Intelligent Character Recognition) is used to recognize hand-written text.
What are the benefits of OCR?
OCR can help save time and reduce errors by automating the process of converting scanned documents into machine-readable text. It also makes it possible to search for and extract specific information from scanned documents.
What are the limitations of OCR?
OCR is not perfect and may make mistakes, especially when the text in the image is of poor quality or has a different font, style or size. Additionally, OCR may have difficulty recognizing text in languages with non-Latin characters.
What is machine learning in the context of OCR?
Machine learning is a subset of artificial intelligence that involves training a computer model to make predictions based on data. In the context of OCR, machine learning can be used to improve the accuracy of text recognition by training the computer model on a large dataset of text images.
How does machine learning improve OCR accuracy?
Machine learning algorithms analyze large datasets of text images to learn patterns and relationships between visual features and text characters. This information can then be used to make more accurate predictions when recognizing text in new images.
What are some popular machine learning algorithms used in OCR?
Some popular machine learning algorithms used in OCR include neural networks, decision trees, random forests, and support vector machines.
How is OCR used in business?
OCR is used in business to automate the process of extracting information from scanned invoices, receipts, and other documents. This can help save time and reduce errors, making it easier to manage financial information and make more informed decisions.
How is OCR used in the healthcare industry?
OCR is used in the healthcare industry to extract information from medical records, insurance forms, and other documents. This information can then be used to improve patient care, monitor treatment outcomes, and reduce administrative burden.
What are some common OCR software applications?
Some common OCR software applications include Adobe Acrobat, ABBYY FlexiCapture, Readiris, and OpenCV.
What is the cost of OCR software?
The cost of OCR software varies widely depending on the features and capabilities offered. Some basic OCR software is available for free, while more advanced software can cost hundreds or even thousands of dollars.
What is the accuracy of OCR software?
The accuracy of OCR software can vary widely depending on the quality of the image, the type of text, and the OCR software being used. However, OCR accuracy has improved significantly in recent years due to advancements in machine learning.
What are some of the challenges of using OCR with machine learning?
Some of the challenges of using OCR with machine learning include the need for large amounts of high-quality training data, the difficulty of fine- -tuning the machine learning model, and the challenge of integrating OCR with other technologies and systems.
Can OCR be used with other technologies like Artificial Intelligence and Natural Language Processing?
Yes, OCR can be used with other technologies like Artificial Intelligence and Natural Language Processing to improve the accuracy and functionality of the OCR process. For example, combining OCR with NLP can help extract and analyze structured information from unstructured text data.
What is the role of deep learning in OCR?
Deep learning is a subfield of machine learning that involves training neural networks with multiple hidden layers to recognize patterns in data. In the context of OCR, deep learning can be used to improve the accuracy of text recognition by detecting complex patterns in images and text.
How does OCR support accessibility for people with disabilities?
OCR can support accessibility for people with disabilities by enabling them to access text information in a machine-readable format. This can make it easier for individuals with visual impairments to use information and participate in various activities.
What are some of the ethical considerations around OCR and machine learning?
Some of the ethical considerations around OCR and machine learning include privacy, bias, and the potential for misuse of personal information. It is important to ensure that OCR and machine learning technologies are developed and used in a responsible and ethical manner.
What are some of the future trends in OCR and machine learning?
Some of the future trends in OCR and machine learning include the integration of OCR with other technologies like NLP, the development of more advanced machine learning algorithms, and the use of OCR and machine learning in new applications and industries.
How can businesses benefit from OCR and machine learning in the long term?
Businesses can benefit from OCR and machine learning in the long term by automating time-consuming and error-prone processes, improving the accuracy of information, and enabling better decision making. This can result in increased efficiency, productivity, and cost savings for businesses.
For instance at Virtualia Shop, OCR is used to extract product data from product information labels.
Summary
OCR (Optical Character Recognition) is a technology that enables the recognition and extraction of text from images and other forms of visual media. The integration of OCR with machine learning technologies like deep learning has improved the accuracy and functionality of OCR, making it useful for automating processes, improving information accuracy, and enabling better decision making for businesses.
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