OCR (Optical Character Recognition) is a technology that identifies characters from printed books, handwritten papers, or images. With this technology, businesses and users can rapidly transfer documents into their digital systems, and data analysis tools can process the relevant data.
In 2021, OCR still provides outstanding results only on particular use cases. In most practical applications, it is still far below human level accuracy. Modern OCR applications are especially poor in processing documents with poor image quality, some alphabets like less commonly used Arabic fonts, handwriting and cursive handwriting.
1. Which technology advancements provided today’s OCR technology?
- Computer vision
With computer vision technologies, OCR first detects characters one by one. Afterward, it uses image classification to identify each character. If these two steps work successfully, OCR outputs accurate results. However, characters can sometimes be too close to each other and might not be recognized. Thus, OCR requires more than computer vision technologies.
- Natural language processing (NLP)
Even though OCR identifies characters, those characters form words, sentences and paragraphs. Research in NLP has resulted in numerous algorithms that can be used to correct mistakes in character recognition using probabilistic approaches. For example, despite missing characters can be estimated using context.
See more: Natural language processing
- Supervised deep learning
OCR leverages deep learning algorithms to improve its performance. While it requires learning from training samples to improve OCR performance, with this technology, OCR tools can:
Recognize characters with different fonts. Each character can be written in a wide range of forms, and large labelled data set help OCR software identify the characters despite font variations.
Detect errors and correct them. OCR tools can skip characters that cannot be identified. By recognizing patterns in training samples, OCR can detect those errors and correct its mistakes.
2. What are the limitations of OCR tools?
OCR is not a stand-alone solution in human-machine communication
The main problem with OCR is that it only outputs unstructured characters. This necessitates the combination of other machine learning technologies into OCR. By that, users can reach structured data from their documents. Our article on data extraction explains how companies can use more advanced technologies to get structured data from documents.
Even with high-quality documents, OCR tools can make mistakes because there are a variety of document formats, fonts, and styles for each character.
3. How to measure OCR accuracy?
OCR accuracy can be measured by the portion of characters in a text that the OCR tool can extract without mistakes. For example, 99% accuracy means that 990 out of 1000 characters are correctly recognized.
4. Ways to surpass these limitations?
OCR has evolved and it is used in almost every major industry now. As it still has areas to be improved, research in OCR has continued. Advances in computer vision and deep learning algorithms contribute to the increased accuracy of this technology. Right now, OCR tools can reach beyond 99% accuracy in typewritten texts. However, higher accuracy levels are desired as companies still make use of human intervention to check for potential errors.
5. The current focus of research in 2021
The current focus of research in OCR technology is mostly on handwriting recognition and cursive text recognition.
- Handwriting Recognition
The research on handwriting recognition also leverages the dynamic motion created during the handwriting process to identify characters. While the main problem with handwriting recognition is the variety of character styles, OCR accuracy in this area is constantly but slowly improving.
- Cursive Text Recognition
The joined letters are clearly harder to recognize than printed texts. This situation brings more errors in OCR tools, and these shapes of the letters do not provide enough information to allow the software to perceive them correctly.
6. Choose OCR Vendors
OCR is still a foundational technology as today’s AI vendors rely on it to extract data. While choosing an OCR vendor, you should consider the following factors:
- Character recognition accuracy
- User-friendly interface
- Computation speed
- Output file formats (Word, Excel, PDF, etc.)
- Integration with ERP data
- Learning over time
If you want to know more about OCR tools, please contact us for more information and detail consult.