Portable 3.46 inch Translator 112 Languages Record Voice 99% Accurate Scan Language Translation Reader Pen Smart Translator
Using the latest technology:
1. Adopt the latest OCR text recognition technology;
2. Self-developed graphics recognition algorithm technology;
3. Adopting China's latest TTS speech recognition technology.
Using the latest 4-core ARM Cortex-A9 2GHz chip, with powerful TTS and audio translation technology, to ensure accurate translation, accurate pronunciation, fast scanning ability, and the speed only needs 0.5s
What is an optical character recognition algorithm and why is it useful?
Optical Character Recognition (OCR) is a type of annotation that allows images of typed or handwritten information to be transcribed into machine-readable text.
Although OCR is often overlooked, it is an irreplaceable helper when we talk about automation. It eliminates the flow of unnecessary paper documents. It allows you to classify, organize, store, manage and share information while avoiding the security risks associated with the physical nature of paper documents.
The availability of OCR has become wider. You must have seen it in movie ticket scanners or airports and train stations. It is used for data extraction and security monitoring (think car license plates or street signs). Electronic signatures are another form of OCR. But arguably the most common use of OCR is to convert images of business documents into digital text that can be searched, edited, and managed.
Let's imagine a situation. You are attending an important meeting. Your business partner shows you a document; you pull out your smartphone and take a quick photo. You seem to have the information you need, but it's in the form of an image. You cannot use this document directly. Instead, you need to convert the pixels of the photo into a readable format so you can edit and manipulate the information it contains.
Furthermore, OCR-based automation is not just about sharing information in digital form. When you have a lot of documents, machines can use them as data entries to find patterns and trends. Visualization has also become easier: if you need diagrams, schemes, or spreadsheets, using digital documents is much faster than writing a visually pleasing report by hand. OCR allows you to spend less time processing each new document, saving labor costs and focusing on value-added strategies.
How does the OCR algorithm work?
People are very good at recognizing text characters, even if they are handwritten. For a machine, however, this is a tall order. They need machine learning algorithms to learn how to read how people read. To this end, OCR algorithms require extensive training to process text images.
In order to understand how the OCR algorithm works, first we want to tell you more about text and its properties. Why? Because that's how machines see text: as part of an image.
Text Properties of OCR Algorithms
There is a big difference between the text you can find in a commercial setting and the text that exists "in the wild": in the form of street, handwritten notes, captcha, etc. One in the well-structured, uncluttered scan quarterly report is miles away from random graffiti caught on camera by surveillance drones. However, these two examples demonstrate many properties that help explain text images to machine learning algorithms.
Density. In document scans, text is often denser than text on street corner photos.
Structure. The difference is the difference between ordered lines of printed text and poor structure (or lack thereof) in a handwritten shopping list.
Font and size. Rigid fonts and letters of the same size are more recognizable than street signs with an inconsistent or freehand style of handwriting.
Character type. This property indicates not only the presence of letters, but also the presence of numbers, symbols, and special characters. Also, language is important. A document usually consists of one language; on the other hand, a sign or graffiti can contain information in multiple languages.
Noise. It is important to pay attention to how the image is obtained (scanned or photocopied documents; photographed signs and license plates). Depending on the method, photos tend to produce more noise than scans.
The position and alignment of the text on the image. The scan is usually front and center with little tilt. Photos, on the other hand, don't offer any strict layout: text can be in any part of the image, and it can be taken from the side.
As you can see, text is not just a few lines of characters. Naturally, text attributes help build the nuances of OCR algorithms.
Now that we know how text is different, let's see how to build an OCR algorithm.
The process of building, labeling and training text recognition algorithms
Build, Label, and Train Text Recognition Algorithms Build, Label, and Train Text Recognition Algorithms
Building an OCR algorithm from scratch takes many steps.
Tip: This is a short overview of the main steps required to build an OCR engine. If you want a more detailed breakdown, follow this link to read a long article on the AI project life cycle.
— Step 1. Collection
The first thing you need to do is gather a database of documents. You can already have paper documents that you want to digitize. However, in order to build an optical character recognition algorithm, you need to choose a sufficiently large representative sample. This means that the set of documents you choose should be relevant to your end goal.
In addition, this step includes scanning, copying or photographing documents. If the images are of high quality, it will greatly benefit and facilitate the training process. Read more about good dataset characteristics in our article.
— Step 2. Preprocessing
Before starting to recognize text, document images must be prepared, cleaned and optimized for OCR algorithms. There are many problems that can cause poor image quality: insufficient lighting, paper flickering and reflections, poor camera or scanner quality, skewed angles, missing characters or poor print quality, etc.
If you want to properly train the OCR algorithm, you should consider doing the following before the next step:
Convert the image to black and white. Removing colors can reduce ambiguity in text detection.
Straighten and align. Odd angles significantly complicate the detection process.
Cut and center text. Leave only the important parts: the text should be front and center, not hidden somewhere in the corners.
Apply filters to reduce noise. Individual characters should stand out from the background. Remember that scans are usually sharper than photos.
— Step 3. Data Labeling
This is a critical step in the OCR algorithm, and that's where we're here to help you. The text recognition process consists of two tasks: detection of text and recognition.
We use boxing to highlight and outline the text area. This tells the OCR algorithm what to look for in the image.
Our annotators then transcribe (manually enter text) on the images. Later, OCR algorithms will be able to use image classification to find patterns between pixel sets and character types.
In addition, we also conducted several rounds of QA. People are much better at recognizing text in images than machines, but even then we want to make sure nothing is missed.
This step of data labeling takes a lot of time and effort, but you don't have to worry about it. We'd love to take this task off your shoulders. Data annotation for OCR tasks is one of Label Your Data's features. We've done it before and we'd love to do it again for your OCR project. Call us today to learn more!
— Step 4. training
Now that you have annotated documents, you can start training the OCR algorithm. This step depends on the type of strategy you use to build your OCR algorithm. These strategies vary widely, from classical computer vision techniques to specialized deep learning methods based on building neural networks.
Each strategy has its advantages. But no matter which method you choose, ML algorithm training usually doesn't work on the first try. Retraining and improvement are common practices. Don't be discouraged if the OCR algorithm doesn't immediately provide perfectly accurate text recognition. With practice and persistence, you'll get there!
— Step 5. Post-processing and quality assurance
In fact, if you don't want to do everything all over again, you need to QA every step of the way. But this is the final QA step and make your OCR algorithm work. It's time to reap the fruits of your hard work and finally digitize your document workflow, saving your business time and money.
Although not often discussed outside the machine learning industry, optical character recognition has one of the highest usability ratings in AI. Businesses still operate based on massive amounts of paper documents, an outdated and almost harmful practice. OCR can help businesses deal with it by digitizing the workflow.
In addition, the scope of application of OCR does not stop there. Any text, whether it's a neatly arranged report, a random store sign, or a handwritten note, can be processed by OCR and converted into machine-readable text. This is a step towards big data automation.
Oddly, while building text recognition algorithms isn't a new technology, it's as challenging as ever. Of course, open source OCR algorithms are available to the public. However, if you want a state-of-the-art text recognition model for your specific purpose, it's best to build one yourself. We can help you! Tell us about your project and we'll professionally annotate the documents to train your OCR algorithm.