The Real Hype Of AI In Retail Market And Ecommerce

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.

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.gMicrosoft 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 data collection 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. 

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 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 with reference to Artificial Intelligence.

Data Collection For OCR Using Artificial Intelligence.

On a broader scale, AI training dataset 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 OCR data collection 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.

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 image data collection which helps you to enhance the performance of your machine learning models. 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. 

Optical Character Recognition and GTS

Today, optical character recognition plays an important role in many businesses' digital transformation processes, allowing them to store data securely and retrieve information more easily. Marketing firms also use OCR algorithms to increase customer engagement and sales by providing a unified buyer experience. Aside from helping businesses, OCR benefits the environment by reducing the number of hard copies of important documents and thus saving paper. Last but not least, OCR aids in the translation of the written text into a variety of languages, increasing document accessibility and bridging the language gap. As a result, Global Technology Solutions offers OCR training dataset for your AI and ML models along with video dataset collection and annotation services for your business need.