What is an OCR?
Ever thought of converting a physical document into an editable and machine-readable format? Imagine converting the text contained in the images into a machine-readable format. That is what OCR or Optical Character Recognition does for you. The technique can convert texts from varied sources like receipts, invoices, and vehicle license plates through image to text converter capabilities. By utilizing OCR, these texts are extracted and reformatted, making them accessible for computer systems to efficiently read, edit, index, and subsequently archive for future reference.
How Does OCR Work?
OCR in its early days depended on basic computer vision methods like threshold and contour identification to extract text from images. Things have changed with the new developments in the technology.
New OCR tools now make use of sophisticated deep-learning techniques to locate and extract the text from the images. These tools are trained with thousands of images and corresponding text to help improve their effectiveness.
Choose The Right Python Library for Extracting Text From Images
If you are a Python enthusiast like me, you will find access to numerous open-source Python libraries. Tesseract is a clear leader in this arena. It offers you access to several advantages such as making the best use of convolutional and recurrent neural networks. This makes it the best choice for sequential data interpretation.
There is another great option in the form of Attention OCR, based on TensorFlow, which excels in understanding longer textual sequences within images. However, it would need a complete understanding of TensorFlow.
If you are looking for a user-friendly alternative, our own experience suggests using the Nanonets OCR API. You can use it to build custom models and obtain predictions in a user-friendly format without needing extensive knowledge in machine learning or OCR.
Are Online OCR Tools Reliable?
In today’s world which is driven by data, choosing the best OCR tool has become a tough task. The high accuracy is essential for an efficient performance. The Nanonets OCR API is an excellent choice in this context.
The tool is trained on a diverse data set. It offers you support for both security and privacy requirements. Yet another excellent option that you would find impressive can be onlineocr.net, for provides you with effective and precise text extraction features.
The Top-End OCR Software In The Market Today
Some of the best OCR tools that you can find in the market today include Abby FineReader, Adobe Acrobat Pro DC, and Nanonets. In my first job, I used Abby FineReader to process 1000s of images and extract text out of them. However, they come with a disadvantage as we have experienced in during our exposure to them. They do not offer customized solutions as they fail to support every image format.
These tools may also struggle with poor-quality images or irregular text formats. Nanonets, on the other hand, provides excellent service quality offering the creation of custom models capable of handling challenging image conditions with high accuracy and efficiency.
Best Use Cases of OCR
OCR technology finds its application in various fields, including:
- Vehicle Registration Plate Recognition: Useful in enforcing traffic regulations, tracking vehicles in parking lots, and improving security in various areas.
- Legal Document Digitization: Assisting in different legal paperwork like affidavits and legal rulings.
- Automated Table Recognition: Identifying tables in documents for data analysis, entry, and gathering.
- Banking Applications: Processing cheques, updating financial records, and ensuring regulatory compliance in banking services.
- Restaurant Menu Conversion: Translating restaurant menus into standardized templates for different languages for delivery services.
- Healthcare Records Management: Converting patient history into digital formats for easy access by medical professionals.
- Automated Invoice Processing: Streamlining the extraction of information from bills and invoices in the retail and logistics sectors.
Financial Implications Of OCR Implementation
It is observed that implementing Nanonets’ OCR for invoice digitization has helped in saving the time needed for processing the invoices by up to 90%. You may witness a marginal decrease in accuracy, but we found not a huge difference as such.
It was observed that it enhanced the customer experience considerably. Apart from this, the tool was also found to enhance the productivity of the employees by making their work less monotonous.
Conclusion
Making use of an image-to-text converter like OCR has definitely transformed the way we process data across multiple industries. The options such as precision, speed, and adaptability make it an essential tool in our increasingly digital world.
The key to success in making the OCR an integral part of your business lies in making the right choice of the OCR tool for your needs. The Nanonets’ OCR ideally works quite great in this context.
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