Optical Character Recognition (OCR) has been around for a long time. The early use of OCR technology dates back to the early 20th century. The technology was developed to extract words from images and form sentences using those words. Companies across the globe use it to eliminate the manual processing of documents.
The emergence of Artificial Intelligence helped boost the functionalities of OCR. The technology that aims to convert printed documents into machine-readable texts can now recognize various handwriting styles and languages. It is revolutionizing the ways machines read texts.
In this blog, we’ll take a deep dive into OCR and machine learning, exploring what the future holds, the advantages, challenges, and a few real-world applications.
Optical Character Recognition is not a new development. As mentioned earlier, the technology has been around since the early 20th century. However, with time, it had evolved rapidly. Initially, OCR was developed to automate the reading of printed text and was rule-based, heavily dependent on handcrafted materials.
As technology evolved, the use of machine learning for OCR improved the technology. Initially, the use of convolutional neural networks (CNN), followed by recurrent networks, and now transformers, brought a breakthrough. Today, OCR with deep learning can scan print, handwritten, and scene text, providing highly accurate results.
Compared to traditional OCR, modern technology can learn from image data and produce results.
Here’s what made this possible:
→ CNNs – Good at learning spatial hierarchies from image data, which is a key factor for accurate text and character recognition
→ RNNs and LSTMs – Helps understand model sequence dependencies, making line and text recognition in cursive writing simple
→ CRNNs (Convolutional Recurrent Neural Networks) – A combination of spatial understanding and temporal modeling
→ Attention Mechanisms and Transformers – Improved sequence modeling by letting the model focus on specific parts or sections of images or texts, making recognition accurate in complex layouts
These will help you understand how OCR models have evolved and have enabled accurate results from various types of image and text data. Before we look into the other aspects, let’s understand the concept of deep learning OCR in the following section.
What is OCR Deep Learning?
A. Data Collection
Datasets for images containing text are collected for training purposes.
B. Model Training
CNNs or RNNs are trained on these datasets to learn how to accurately recognize and interpret text.
C. Feature Extraction
The models then identify patterns and features within the images that correlate with text characters.
D. Text Recognition
The trained models can accurately recognize and interpret text from images or documents, even in challenging situations.
The use of deep learning has improved OCR, and numbers speak of how the technology is getting used rapidly. Researchers expect the global OCR market to grow at a CAGR of 17.23% in the next few years and reach $43.69 billion by 2032.
What made machine learning character recognition so popular? Let’s investigate that in the next section.
The Advantages of Deep Learning in the OCR Model
Deep learning models have improved OCR in more than one way. It has made text detection easy for machines. Now that you understand how, let’s take you through the advantages of deep learning-based OCR systems:
A. Improved Productivity and Efficiency
OCR has automated the process of converting printed or handwritten text into formats that are editable by machines. This way, it reduces the time and effort necessary for manual data entry. OCR systems have made it easy for organizations to streamline their documents and also help in searching and sorting them.
B. Enhanced Accuracy and Reduced Errors
The OCR technology is used for text recognition to make it editable for machines. With deep learning, the technology can now accurately detect text in noisy, distorted, or low-resolution images. The models learn from various datasets, making them perfect for different fonts, styles, and scene text.
C. Support for Handwritten Text
Traditional OCR was not well-trained in handling cursive or handwritten content. Deep learning techniques have helped improve technology’s ability to understand inconsistent, messy, or handwritten content.
D. Multilingual and Script Flexibility
The deep learning OCR model architecture can be trained on multiple languages and scripts. This helps the technology adapt to multiple global use cases, unlike manual configurations where manual adjustments are necessary.
E. Contextual Understanding
Deep learning OCR can interpret complex document layouts. The use of attention mechanisms and transformer models makes it possible for deep-learning OCR to interpret such layouts.
F. End-to-End Learning
Deep learning techniques are end-to-end. The OCR models can learn everything, including feature extraction, sequence modeling, and text prediction, all at once. There’s no need for manual engineering in the entire process.
G. Real-Time and Scalable
Numerous OCR systems can run in real time and on mobile devices. Immediate translations, document scanning, and use for deployment in logistics, retail, and manufacturing.
H. Continuous Data Improvement
Using deep learning models can be beneficial in retraining or fine-tuning OCR. It paves the way for continuous improvement without using rules. This was something traditional OCRs could not do.
That makes deep learning OCR a better option for businesses and organizations. Companies often seek data annotation services to assist them with the entire process.
The Limitations of Deep Learning OCR
Despite OCR deep learning models being the best way to extract text from images, there are a few limitations. Let’s take a look at them:
A. Quality of Images
The accuracy of text recognition is highly dependent on the quality of images. In case the image is of low quality, the entire purpose of text recognition diminishes.
The only way to overcome this limitation is by ensuring the image is of high quality.
B. Data Dependency
Deep learning OCR models require high-quality, labeled datasets for effective training. Data for rare languages, scripts, and niche document types can be hard to find, and creating them can be time-consuming and expensive.
C. Language and Script
Most deep learning based OCR models are optimized for the Latin alphabet. As a consequence, these models are not trained for complex texts. The work to train the model to understand complex scripts is still in progress.
D. Requires High Resources
Deep learning OCR utilizes powerful GPUs and substantial computational resources. That makes it difficult to run this model in every setting. This is one of the major limitations of using the OCR model.
E. Context Understanding
The model is trained to understand text in images. It can recognize characters, but cannot comprehend what is written there. Without natural language processing, the model might not be able to interpret characters or words.
F. Layout Changes
One of the major limitations of this character recognition model is that it cannot recognize layout changes. It cannot recognize tables, multi-column formats, rotated texts, etc. without specialized layout analysis models.
G. Security Problems
Minimal changes can trick the advanced character recognition model. Small changes in images can confuse the model, leading to incorrect results.
Despite the limitations, the technology is set to change the way machines read text. In the following section, we will look into what the future holds for OCR.
What Does the Future Hold for Deep Learning-Based OCR?
The OCR technology was upgraded using machine learning techniques. With machine learning involved, text extraction is easier. Like geospatial annotation, modern and updated OCR technology makes it easy for machines to read handwritten or other texts from images. Considering the fact that technology is always evolving, let’s look into what the future holds for the OCR model performance:
A. Recognizing Multiple Languages and Scripts
In the previous section, we discussed how understanding different languages is tough for the OCR model. The future talks about the possibilities of improving the system to ensure it understands multiple languages and scripts. Further, the end-to-end models are expected to be better at code-switching or understanding mixed language sets.
B. Understanding Layout and Structure
OCR models are being trained to understand more than just text extraction. It will be able to understand hierarchical layouts, tables, and form elements. It is one of the best implementations in intelligent document processing (IDP) for sectors like finance, law, and healthcare.
C. Integration with Large Language Models (LLMs)
The technology is being developed to ensure context-aware textual recognition using Large Language Models (LLMs). In the future, it is expected that OCR will extract text, and LLMs will handle interpretation.
D. Real-Time and Edge Deployment
Model compression and efficient architectures are making OCR compatible with mobile and edge devices. AR translation, warehouse automation, and smart glasses are just a few examples of use cases that are shaping the future of OCR technology.
E. Self-Supervised and Few-Shot Learning
Self-supervised learning will be the future for OCR systems, reducing the dependency on labeled data. Few-shot and zero-shot learning helps generalize unseen fonts, languages, and other concepts.
F. 3D and Scene Text Recognition
The technology is being developed to understand and recognize texts in natural scenes, 3D environments, and augmented reality (AR) and virtual reality (VR) settings. This will increase the reach of OCR in recognizing distorted, occluded, or curved texts.
That is all about deep learning OCR. The technology is one-of-a-kind. Despite being a modern and updated technology, we all have used it. We all use our phone cameras or scanners to scan documents and then save them as PDFs or Word documents. This is a classic example of deep learning OCR that we have all been using for quite some time.
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Frequently Asked Questions
Is OCR supervised or unsupervised?
OCR using machine learning techniques is trained to recognize patterns and the meaning of content, following a few rules. This is possible using supervised, unsupervised learning, or a combination of both.
Does OCR use CPU or GPU?
Classification and extraction tasks are run using the CPU. However, it is recommended that OCR should be run using a GPU. You will also get a CPU version.
Why does OCR fail?
OCR accuracy can be affected by various reasons. Here are a few of them:
→ Low-resolution scans
→ Poor image clarity
→ Text distortion
While these limitations cannot be denied, work is in progress to improve the system and overcome these hurdles.
Does OCR need the internet?
OCR is an offline process used to scan and recognize text in static documents. However, there are cloud-based services that provide online OCR API services.
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