What Is Optical Character Recognition and How Does It Work?



Introduction

Learn how to use Optical Character Recognition to convert any type of image containing written text into machine-readable text data, from handwritten content to printed text and image-only digital documents. The growing presence of digital media in the twenty-first century has resulted in an increase in the demand for digitized documents. Digitally stored documents have significant advantages over their "real world" counterparts, particularly in terms of physical space occupied and security. As a result, Document Analysis with AI Training Dataset for document digitization has become an integral part of computer vision and a rapidly developing field of research. OCR, or optical character recognition, is a critical component of document digitization.


Here's what we'll talk about:

  • What exactly is Optical Character Recognition (OCR)?

  • What is the process of Optical Character Recognition?

  • Applications for Optical Character Recognition

  • Business Advantages of Optical Character Recognition

  • Key Takeaways from Optical Character Recognition


Let's start with the fundamentals.

What exactly is Optical Character Recognition (OCR)?

The process of detecting and reading text in images using computer vision is known as optical character recognition (OCR). Text detection from document images allows Natural Language Processing algorithms to decipher the text and understand what the document is saying. Furthermore, the text is easily translatable into multiple languages, making it accessible to anyone. OCR, on the other hand, is not limited to detecting text from document images or we can say collecting image dataset. New OCR algorithms use Computer Vision and Natural Language Processing (NLP) to recognize text from supermarket product names, traffic signs, and even billboards, making them effective translators and interpreters. In the wild, OCR is frequently referred to as scene text recognition, whereas the term "OCR" is generally reserved for document images only. Under the umbrella of OCR, we will investigate both document text extraction and scene text recognition in the future.

What is the process of Optical Character Recognition?

Traditional image processing and machine learning-based approaches, as well as deep learning-based methods, can be used to develop optical character recognition algorithms.


Conventional OCR: While traditional machine learning-based approaches are quick to develop, they take much longer to run and are easily outperformed in terms of accuracy and inference speed by deep learning algorithms. Traditional OCR approaches involve a series of pre-processing steps in which the inspected document is cleaned and noise-free. The document is then binarized for subsequent contour detection to aid in the detection of lines and columns. Finally, the characters that comprise the lines are extracted, segmented, and identified using machine learning algorithms such as K-nearest neighbors and support vector machines. While these work well on simple OCR datasets such as easily distinguishable printed data and handwritten MNIST data, they lack many features that cause them to fail when used on complex datasets.

Deep Learning and OCR: Deep learning-based methods can extract a large number of features more efficiently than machine learning methods. Algorithms that combine Vision and NLP-based approaches have proven to be especially effective in providing superior results for text recognition and detection in the wild. Furthermore, these methods provide an end-to-end detection pipeline, removing the need for lengthy pre-processing steps. In general, OCR methods include vision-based approaches for extracting textual regions and predicting bounding box coordinates. The bounding box data and image features are then passed to Language Processing algorithms, which decode the feature-based information into textual data using RNNs, LSTMs, and Transformers. Deep learning-based OCR algorithms are divided into two stages: region proposal and language processing.
  1. Proposal for a Region: The detection of textual regions in an image is the first stage of OCR. This is accomplished by employing convolutional models that detect text segments and enclose them in bounding boxes. The network's task here is similar to that of the Region Proposal Network in object detection algorithms such as Fast-RCNN, in which potential regions of interest are marked and extracted. These regions are used as attention maps and are fed into language processing algorithms along with image features extracted.
  2. Linguistic Processing: RNNs and Transformers are NLP-based networks that extract information captured in these regions and construct meaningful sentences based on features fed from the CNN layers. Recent works have successfully explored fully CNN-based algorithms that recognize characters directly without going through this step. These algorithms are especially useful for detecting text that has limited temporal information to convey, such as signboards or vehicle registration plates.


Applications for Optical Character Recognition

OCR has found use in a variety of industries, including banking, legal, and healthcare.

Here are a few applications of Optical Character Recognition.

  1. Identification of the document: Document identification is a critical application of OCR, with the detected text used to categorise documents into groups, making access infinitely easier and faster.
  2. Automation of data entry: Data can be efficiently captured from documents and tables using OCR, making manual data entry obsolete. Automation of data entry with OCR reduces data anomalies caused by typos. Furthermore, data extraction becomes extremely fast and inexpensive.
  3. Development of archives and digital libraries: OCR aids in the creation of digital libraries by identifying the classes to which a book or document belongs. These classes (or genres) can be used to look up a specific category of books, allowing the reader to easily navigate the list. Similarly, OCR aids in the digitization of old documents, making preservation extremely simple and secure.
  4. Translation of text: Text translation is an essential component of OCR, especially for scene text recognition and evaluation. Translation modules stacked on top of an OCR system's output can assist international tourists in understanding documents and billboards in different languages.
  5. Recognize sheet music: Text detection systems can be taught to recognise sheet music from notations, allowing a machine to play music directly from the text. This enables machines to teach aspiring musicians and can also be used for ear training exercises.
  6. Advertising campaigns: FMCGs have successfully used OCR systems in marketing campaigns by attaching a scannable text section to their products. This text section can be converted to a textual code to redeem promo codes when scanned by a mobile camera or capturing device.

Business Advantages of Optical Character Recognition

There is no doubt that the OCR will be used by an increasing number of businesses in the coming years. The following are some of the advantages of this technology for businesses.


Manual data entry is no longer required: OCR eliminates manual data entry by allowing data to be identified directly from document images. As a result, it reduces data entry time and reduces data processing errors.

Improved searchability and accessibility: OCR scanned documents can be easily indexed, making them searchable among many other documents. When compared to their physical or photographic counterparts, they can be indexed by their content, titles, or even specific keywords.

Additional storage space: OCR aids in the digitization of documents, thereby increasing storage space. Documents do not have to be kept in physical or image form; they can be kept in text form, which is much smaller.

Optical Character Recognition and GTS

Today, optical character recognition plays an important role in many businesses' digital transformation processes, allowing them to store data securely and retrieve information more easily. Marketing firms also use OCR algorithms to increase customer engagement and sales by providing a unified buyer experience. Aside from helping businesses, OCR benefits the environment by reducing the number of hard copies of important documents and thus saving paper. Last but not least, OCR aids in the translation of the written text into a variety of languages, increasing document accessibility and bridging the language gap. As a result, Global Technology Solutions offers OCR training dataset for your AI and ML models.


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