Introduction
Deep learning technologies have been sweeping the globe and all kinds of companies, including large tech companies, well-established businesses as well as startups are looking to integrate deep learning (DL) and machine learning (ML) within their workflows. One of these solutions that has been gaining a lot of attention over the past couple of months is an OCR engine.
OCR (Optical Character Recognition) is a method of extracting textual data straight from electronic documents as well as scanned documents, without the need for human intervention. The documents can be in any format such as pdf, PNG, JPEG, TIFF and more. There are many advantages of making use of Dataset For Machine Learning and OCR methods, including:
1. It increases productivity because it takes a lot shorter period of time in order to analyze (extract details) those documents.
2 It saves resources as you only need an OCR program to do the job and no manual labor will be needed.
3 It eliminates the requirement to manually enter data.
4. The chance of error is reduced.
Finding information in digital documents is not difficult because they contain metadata that gives you the information about text. For scans, you need another solution, as metadata doesn't help in that case. There is a need for deep learning, which provides solutions to extract text information from images. This article you'll discover the different lessons to be learned from creating a deep learning-based OCR model. This will ensure that when you work on any of these use cases it is unlikely that you'll encounter the issues I've had to face during the process of design and implementation.
What exactly is deep-learning-based OCR?
OCR is becoming very well-known these days and is used by a variety of industries to speed up reads of text from pictures. While techniques such as contour detection, image classification, connected component analysis and so on. are utilized to analyze documents with similar texts in size, font, perfect lighting conditions, great image quality as well as good image quality. However, these techniques aren't suitable for heterogeneous, irregular text commonly referred to as "wild text" or "scene text. The text could come from a vehicle's license plate or house number plate document scans that are not well-scanned (with no set of requirements) or any other document. To do this, Deep Learning solutions are employed. Making use of DL to perform OCR is a 3-step process and the steps include:
Preprocessing: OCR cannot be described as an simple problem, at the very least not as straightforward as we imagine it should be. Extracting text from documents or digital images is good. But when it comes down to phones-clicked or scanned images, things can change. Images from the real world aren't always scanned or clicked in perfect conditions. They can exhibit blur, noise and skewness. This needs to be addressed prior to applying DL model to these images. This is why image processing is essential to deal with these problems.
Text Detection/Localization: At this stage models like Mask-RCNN, East Text Detector, YoloV5, SSD, etc. are used to find the text within images. They typically produce boundaries (square/rectangle boxes) over the text in the image or document.
Text Recognition: Once it is recognized every bounding box is transmitted to the model for text recognition that is typically a mix with the RNN, CNNs along with Attention networks. The output of such models will be the extracted text out of documents. Some open-source text recognition models like Tesseract, MMOCR, etc. can help you gain good accuracy.
To demonstrate the efficacy and effectiveness of OCR model, we'll take some look at some of the sectors that OCR is being used today to boost the efficiency and efficiency of systems:
- OCR for Banking: Automation of the client verifying, depositing checks etc. processes that use OCR-based verification and text extraction.
- OCR in insurance: Extracting the text information from a range of documents within the insurance industry.
- OCR within Healthcare: Processing the documents such as a history of a patient and x-ray reports or diagnostic report, etc. is a difficult task , but OCR can make easy for you.
This is just a handful of the instances in which OCR Training Dataset is used. To learn more about its application examples, check out the following hyperlink.
Learnings from the development of an OCR model based on deep learning OCR model
Once you're informed about the basics of what OCR is and why it a significant concept in our current world It's time to talk about some of the difficulties you will face when working on OCR. I've been involved in numerous OCR-related projects connected to finance (insurance) area. I'll list a few of them:
- I've been involved in an KYC verification OCR project where data from various identification documents had to be separated and verified against one another to confirm the profile of a client.
- I also have worked on OCR for insurance documents where the information contained in various documents had to be extracted to use for various other purposes, such as the creation of user profiles and verification of users, for example.
One thing I've learned from working on these OCR Datasets applications is that you don't have to be a failure every time you try to gain knowledge about new techniques. You can also learn from others' mistakes too. There were many stages in which I encountered challenges when working as a member of a team for the financial-DL-based OCR projects. Let's look at those challenges through the various phases in ML pipeline design.
- Data collection
- Annotating the data (data annotation)
- Model architecture and infrastructure for training
- Training
- Testing
- Monitoring and deployment
OCR Based Model Training With GTS Datasets
- Global Technology Solutions (GTS) OCR has got your business covered. With its remarkable accuracy of more than 90% and fast real-time results, GTS helps businesses automate their data extraction processes. In mere seconds, the banking industry, e-commerce, digital payment services, document verification, barcode scanning, Image Data Collection, AI Training Dataset, Video Dataset along with Data Annotation Services and many more can pull out the user information from any type of document by taking advantage of OCR technology. This reduces the overhead of manual data entry and time taking tasks of data collection.
