The idea of converting written or printed text into digital text is generally called OCR for optical character recognition. Detecting handwritten characters is a rather difficult task. For now, today’s software and developed algorithms can not achieve 100% accuracy (not even a real person can always recognize what was written). In practice, an OCR application works best when dealing with restricted inputs and/or limited domains. For example, it’s possible to recognize the English names for numbers and the names of major cities, especially if you can get people to write each letter in its own little box. However, that same software wouldn’t work for similar texts in other languages.
Many of today’s handwritten text detection systems are built following traditional approaches to image processing and work great with printed text, but if use them for handwritten text recognition in images it can get unexpected results with poor recognition quality. Our image processing engineers have developed a handwritten text detection prototype applying custom algorithms that give a high level of accuracy, and eliminate most of OCR limitations.
Our solution tackles all challenges of handwritten text detection like separating characters
from background, separating merged symbols, text written in nonlinear rows.
Unlike similar application on the market, ours detects characters of any language, be it Chinese, or Italian.
The algorithm can process frames from your camera in real time.
The algorithm is able to detect handwritten text by processing even blurry, grainy and low resolution images.
The prototype can be used for processing all kinds of paper documents in any industry,
starting from the surveys, document forms, handwritten invoices.
Choose one of the sample images or upload your own