The need for digitized documents has inevitably increased with the prevalence of digital media in the twenty-first century. Documents that are saved digitally have many advantages over those that are kept in the “real world,”, particularly in terms of the space they take up and the security that comes with their use. As a result, computer vision now includes document analysis with AI for digitizing documents, which is a rapidly growing area of study.
What is Optical Character Recognition?
Computer vision is used in the process of optical character recognition (OCR) to find and read the text in photographs.
Natural Language Processing algorithms can interpret the text and understand the message the document is trying to express by detecting text from document images.
The material may also be readily translated into a variety of languages, making it understandable to anybody.
OCR technology, however, goes beyond simply reading text from document pictures. New OCR algorithms combine Computer Vision and NLP to recognize text from billboards, traffic signs, and even supermarket product names, making them great interpreters and translators.
How does Optical Character Recognition work?
Algorithms for optical character recognition might be based on conventional image processing, machine learning, or deep learning techniques.
Finally, using various machine learning methods like K-nearest neighbors and support vector machines, characters constructing the lines are extracted, segmented, and recognized.
These perform admirably on straightforward OCR datasets, such as easily discernible printed data and handwritten MNIST data, but they are severely lacking in features when dealing with more complicated datasets.
OCR with Deep Learning
Deep learning-based approaches are preferable to machine learning approaches because they can efficiently extract a lot of features.
For text recognition and detection in the real world, algorithms that integrate vision and NLP-based methods have been especially effective.
Additionally, these techniques offer a complete detection pipeline that frees them from time-consuming pre-processing stages.
Generally, OCR methods include vision-based approaches used to extract textual regions and predict bounding box coordinates for the same.
The region proposal stage and the language processing stage are the two steps of deep learning-based OCR systems.
Region Proposal: The initial step in OCR entails identifying text-rich regions in the image. Convolutional models that recognize text fragments and enclose them in bounding boxes are used to achieve this.
Language processing: RNNs and Transformers, two NLP-based networks, attempt to extract information from these regions and create comprehensible sentences using characteristics fed from the CNN layers.
What makes OCR work well?
Text detection algorithms are used as the foundation for contemporary OCR techniques. Modern neural networks have improved significantly in their ability to recognize text in documents and photos, even when it is tilted, rotated, or skewed.
Following these two procedures can help you increase OCR accuracy:
To avoid non-textual regions being presented as text, data input to the model should be adequately denoised. There are various methods for denoising, with Gaussian blurring being the most common. An auxiliary autoencoder network can also be used to eliminate additive white noise.
The neural network’s ability to distinguish between text- and non-text-containing regions is greatly aided by image contrast. The OCR process performs significantly better when the contrast between the text and backdrop is increased.
Optical Character Recognition Applications
Numerous industries, including banking, law, and healthcare, have found uses for OCR.
Here are some usage scenarios for optical character recognition.
Document identification: OCR has many applications, including document identification, where the text that is recognized is used to classify documents into groups, facilitating much faster and easier access.
Data entry automation: OCR makes human data entry unnecessary by effectively extracting data from texts and tables. By automating data entry with OCR, irregularities in the data caused by typing errors are reduced.
Text translation: OCR incorporates text translation heavily, especially for scene text evaluation and recognition. The output from an OCR system can be enhanced with translation modules to aid foreign visitors in understanding signage and documents written in other languages.