OCR Data Collection For Machine Learning


Introduction

Machine learning is becoming a common term among technology students due to its ability to transform the world. Machine learning has several branches, and fields, and OCR is one of such domains related to ML. OCR stands for Optical Character Recognition, businesses can easily deal with digitizing and deciphering scanned images of text. 

We all are amazed by the modern features of extracting text from the captured images from a camera. The technology used behind converting digital images into text and documents is OCR. This technology is widely used in various industries nowadays and it is improving the reach of businesses. OCR is widely used in the industries like logistics, healthcare, tourism, government, etc.

An OCR dataset can help to train a model that can search, index, and optimize data into a machine-readable format. It is using a scanned document dataset to extract information from handwritten documents, invoices, receipts, bills, travel tickets, passports, street signs, medical labels, etc. 

What Is OCR?

Optical character recognition lets you convert scanned images into textual documents from which you can extract data easily. Moreover, digital data files (e.g Microsoft Word documents) already contain digital data, photographed documents are just pixels that need to be converted to digital data before the text is captured from them. With OCR Datasets you can easily collect data to train your model. 

OCR can easily find numbers, letters, and other characters within the pixels of an image irrespective of the format of the image. It identifies the pixels that look like words and characters and then create digital words and characters from them. OCR technology is not necessarily used before data extraction. As we have mentioned above, some PDFs will already be digital, such as those products by an electronic system. For scanned documents, systems, and photos that will rasterize PDFs, OCR is a necessary process for converting text to digital data. An AI training dataset is helpful for making your system error-free and efficient. Let’s now have a look at the data extraction process. 

For example, OCR is used for converting restaurant menus from one language to another eliminating the need for manual translation from a human being. The machine learning model scans the digital images and converts them into textual form to do language convergence easily. OCR can easily find numbers, letters, and other characters within the pixels of an image irrespective of the format of the image.

Data Extraction

Data extraction is a way to find specific pieces of data from a digital document. For example, if you want to have a scanner for passports and want to find the date of birth of a person, you need to find that data within the document. In the case of passports, OCR is often necessary because they are mostly scanned or photographed. Therefore, you need to convert the pixels in the passport photo into digital data and then go through data extraction. After the pixels are converted via OCR, data extraction can find the label and grab the data next to it. 

The OCR Training Dataset can help you to read the images quickly and extract data more efficiently. A document that does not require OCR can directly go through data extraction. Let’s have a look at OCR concerning Artificial Intelligence. 

Data Collection For OCR Using Artificial Intelligence.

On a broader scale, Artificial Intelligence applies broadly to both OCR and data extraction. With the help of AI and tools like machine learning, you can build models that can build to recognize certain types of patterns. These models are what power the OCR or data extraction process. 

Lots of top-notch OCR technologies nowadays use a specific type of AI called ‘deep learning’. The Dataset For Machine Learning can help you to build models that are more efficient and user-friendly. Deep learning produces models that are more effective at converting pixels into data. In the past, things like handwriting were difficult to convert with OCR because it is very valuable. The deep learning models have improved OCR dramatically to an extent that even handwriting can be converted into digital data with great accuracy. 

One can easily apply AI to data extraction, for example, you can use the deep learning models to learn document layouts and the relationships between labels and the text. Through deep learning, the models can distinguish the types of data that people want to extract to narrow down their scope or validate other methods of comprehending candidates available for different data fields.  We are using the OCR data collection technique to collect original images of visiting cards, sign boards, etc., and convert them into machine-readable documents. Businesses and industries can use these datasets to train their machine learning models and increase the impact of their business. With the correct use of OCR a business can easily penetrate the market and reach its target audience. 

AI training datasets can improve the performance of your model and give it a scope for improvement. Let’s have a look at the idea of training models to recognize the date of birth within documents. In this case, if you train the system with hundreds of different documents with date-of-birth, every time the system will see the information is in the form of date of birth. Through this process, the machine learning models can instantly improve the data extraction process because it knows that the corresponding information should be a date. 

OCR Use Cases

There are multiple applications of this technology. Here are some of the used cases mentioned below to give you an idea of how this technology works. 

  • Freestyle handwritten text datasets: You can easily collect thousands of high-quality handwritten datasets in hundreds of languages and dialects to train machine learning (ML) and deep learning (DL) models. We can also help in extracting text within an image. 
  • Receipt: Datasets that consist of invoices where several items were purchased e.g., coffee shop, grocery, restaurant bills, Toll receipts, online shopping, Airport cloakrooms, Fuel bills, taxi receipts, etc. 
  • Multilingual Document: Multilingual handwritten data collection services for computer vision, pattern recognition, and other machine learning solutions to train Optical Character Recognition models. 
  • Scene Data Collection: Medicine bottle with labels, English Road scene with a car license plate, English road scene with instruction board, etc. Transcribe medical labels or drug labels with OCR. 

OCR Datasets

OCR dataset helps you to train real-world applications. It is a form of the dataset which helps you to enhance the performance of your machine learning models. We have collected various types of datasets for the ease of businesses and industries. Here are some of the OCR datasets:

  • Barcode Scanning Video Dataset: In this dataset, there are more than 5K videos of barcodes with a duration of 30-40 sec from multiple geographies. 
  • Invoices, PO, Receipts Image Dataset: Here you will get nearly 15.9K images of receipts, purchase orders, and invoices in five languages i.e, English, Spanish, French, Dutch and Italian. 
  • German And UK Invoice Image Dataset: Here you can get up to 45K images of German and UK invoices. The use case here is invoice Recog. Model. 
  • Vehicle License Plate Dataset: In this dataset, you will get a set of 3.5K images of vehicle license plates from different angles. 
  • Signboard Dataset: This dataset contains almost 877 images of 4 different classes for the objective of road sign detection. 
  • Visiting cards dataset: The dataset contains over 2000 images of original visiting cards captured and crowdsourced from over 300+ urban and rural areas.

OCR Dataset With GTS

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 Datasets 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.

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