#translationtechnology

Optical Character Recognition (OCR) technology got better and better over the past decade thanks to more elaborated algorithms, more CPU power,r, and advanced machine learning methods. Getting to OCR translation accuracy levels of 99% or higher is however still rather the exception and definitely not trivial to achieve.

We learned at Devnagri AI the hard way how to fine-tune our OCR engine to achieve good OCR accuracy and spent weeks fine-tuning our  Image to Text Converter Online engine. So, if you are in the process of setting up an OCR solution and want to know how to increase the accuracy levels of your OCR engine, then keep on reading…

This blog talks about various techniques to enhance OCR accuracy and shares what we learn from building a world-class OCR system for Devnagri AI.

So, First, Let's Define OCR Translation Accuracy

Now when talking about OCR accuracy, there are two ways of measuring how reliable OCR is:

Accuracy on a character level: In most cases, the accuracy in OCR technology is gauged on a character level. The extent to which an OCR software is accurate on a character level is determined by how often it recognizes a character correctly and how often it recognizes a character incorrectly. A 99% accuracy means that 1 out of every 100 characters is uncertain. However, an accuracy of 99.9% means that 1 out of every 1000 characters is uncertain.

Accuracy on a word level: Most OCR translation engines use extra language knowledge about text to enhance their word-level accuracy. That is if the language is known where the text, for instance, is the English language, words recognized can then be compared to the dictionary of all extant words-for example, to all words in an English language corpus. Words containing uncertain characters can then be "fixed" by finding the word inside the dictionary with the highest similarity.

In this blog, we will focus on improving the accuracy of character level. The more accurate characters are recognized, the less "fixing" on a word level is required.

How to Increase Accuracy With OCR Translation Image Processing?

Assuming that you have already settled on an  Image to Text Converter Online engine, we're now down to one single moving part in the equation to improve the accuracy of OCR: 

Good Quality Original Source

Yes, we're repeating this on purpose! The very first simple step in obtaining precise OCR translation conversions is guaranteeing the good quality of the source images. First off, see that the original paper document does not contain tears, creases, or fading or was printed in poor contrasting color ink. In that case, outputting won't be clear enough. Use therefore the cleanest and the most original source of the file to convert.

Scaling To The Right Size

Make sure the images are at the appropriate resolution and are usually of at least 300 DPI (Dots Per Inch). Lowering DPI below 200 results in blurry and unclear images. A higher DPI of above 600 unnecessarily inflates the output file size but fails to improve the quality of the file. Therefore, 300 DPI is appropriate in this regard.

Increase Contrast

Low contrast can lead to poor OCR Translation. Increase the contrast and density before scanning. This can be done directly in the scanning software or any other image-processing software. Increasing the contrast of the text/image with its background brings out more clarity in the output.

Binarize Image

This is a step that converts a multicolored image (RGB) into a black-and-white image. There are several algorithms for converting a color image to a monochrome image, ranging from simple thresholding to more sophisticated zonal analysis.

What We Learned From Building The Devnagri AI OCR Pipeline?

When there is one thing learned about OCR accuracy, there is no silver bullet, and nothing is easier when it comes to shortcutting  Image to Text Converter Online performance. You must inspect the documents carefully before attempting anything. Once you know your shortfalls, you can proceed to the applications of preprocessing steps as aforementioned to improve the accuracy of your OCR translation.

Understanding the new era of artificial intelligence backed by machine learning is complicated. Machine translation is also on the trend utilizing the latest technologies available out there. Companies specifically large enterprises and SMBs are using machine translation API ensuring high accuracy and scalable content. Even government organizations in the management and security as well are into a lot of translation.

At the pace at which AI is developing, we can look at the future easily. Even the news is older about training neural networks on computers to store information and make it available customized and filtered as per requirement. These networks are backed by generative AI but still make mistakes because these networks still cannot be analyzed at a certain human level.

Still, large organizations are processing their operations with a combination of artificial and human-based intelligence. Undeniably, our machine can not start thinking like human beings suddenly but these tools are still on the training model. 

Now if we talk about the translation industry, Machine Translation APIs come into the game where these tools use the generative API and are on tegular training backed by machine learning as discussed in the introduction at the beginning of this blog.

Now what’s new with machine translation APIs during the rise of AI in the market

Size Matters

Now size also matters with an increase in requirements of the translation content in sort of types including the visuals, audios, and videos as well apart from the text. Organizations are translating the contexts not only into official or most popular languages. 

Also, the local dialects to localize their content as well as the business. Training and manipulating the data is part of training models which is important to build a database empowering the robust functionalities into the machine translation API.

Speed is Everything

Now speed is everything in this fast-forward era, here people compete on speed. No one has time, technology is moving forward at a pace that couldn’t have been imagined a few decades back. Every day is something new out there in terms of content in the market. 

Now Generative AI tools in the market whether they are paid or free, no one has a monopoly in the market they all are facing competition. Every giant across the globe has its own AI for the users.

Changes to The Translation Industry

The question arises that the whole will think about the business looking to localize their business in the market. 

Who is sitting to customize the services for their business according to the use case? Also, the people residing deep in the different regions of the world almost everyone is using the internet but everyone is not contributing to it. 

Moreover, most of the content is in English over the content. How the local people will access that content until and unless they know the international English Language?

Summing Up

Far from the large language models here comes the small language models and machine translation APIs that support the website translation and app translation in terms of multiple languages helping the companies to reach their users and distribute their services at a vast level. 

Not only the product and service market but also the market that supports education, hospitality, banking, business, government, security, e-commerce, and media. Keep reading for more.

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Translation technology usage in translation courses in universities

If you were born in the 80’s or the 90’s, you’d very well remember that every subject was limited to theoretical practices, commonly known as books. Even the researchers head to the libraries for taking notes. There were surely other ways such as study tours, special classes, activities and group assignments but there was never even the hint of using technology in the sphere of the education. The only perspective common people had in their minds about technology was that it could make appliances better. Cool new gadgets could be called in-fashion and people would consider them as a technological innovation but that was it. Today, even young students are taught with the help of tools such as projectors, tabs and computers. Technology is now too deep-rooted in our lives that we can’t even imagine living without it. Educational courses are now strongly connected with the very core of IT. Take translation technology courses, diplomas and degrees for instance, most people consider them purely as a medium which can be studied exclusively through traditional ways. However, today universities all across the globe have revived their policies on teaching language courses without incorporating the latest technology into the study material and practices.

MACHINE TRANSLATION, THE GENERAL USAGE

Machine translations from the perspective of a customer are just an easy piece of work. They believe that when a company or a professional uses machine assistance for a project, they are actually defying the purpose of hiring them in the first place. But that’s not the case. Large volume texts are sometimes impossible to translate in a manual way. Moreover, sometimes there are documents and files with recurring texts, words and terminologies and using machine assistance becomes inevitable.

CAT TOOLS; Incorporated Learning

Recently, universities have started teaching language courses along with CAT Tools or Computer Aided Translation technology tools. Students are trained to use these tools while working with large texts with more efficiency. Using computers as a part of their courses is not just to save time and effort but also because it’s impossible for students to choose a career without computer application. CAT comprises four basic elements,

Segmentation

The first and foremost function is dividing the text into segments, making it easier to translate. Each segment is placed in a box and the translation is entered under the source box. This way, time and effort of going through the whole text is saved and work is done quicker than the usual process.

TU (Translation Unit)

Each of the segment, along with the source text box, is saved as a TU or translation unit. With the help of CAT tools, students can navigate their way to these units at any time and utilize the same text again and again according to their requirement.

TM (Translation Memory)

This is said to be the most essential part of CAT tools, preserving the translation memory into the database for later use. A special feature known as the FUZZY Search allows users to even cross check the segments which don’t match 100% with the required search.

Terminology Databases

The fourth function that can be accessed by CAT Tools is looking for the terminologies by inserting them through relevant search items. CAT Tools have thus become a crucial part of almost every translation course at the world’ leading universities. In fact, these tools allow users to utilize automated features even with the projects that have been done manually.

CAT Tools Utility Out Of Machine Translations

Quality checking can also be done through specific features of these tools. They cross match the translated text with the rules and regulations of international quality standards. Students can enter search items and find their required meanings even when they are doing a project without really using the machine. Along with that, they can search for the correct formatting for a certain document or certificate. They can connect to the internet and find a relative terminology that they are looking for. Resulted texts can also be aligned using these tools.

Do All Universities Use Translation Technology In Their Courses?