The unstructured layouts common to such notes make automated content analysis far trickier – as does the inclusion of other types of content, such as mathematical expressions, charts and tables. It must also be able to recognize and decode bi-directional writing – so that it can continue to operate when a writer using a right-to-left language (like Arabic or Hebrew) includes a number of left-to-right foreign words in their content.Ĭursive handwriting makes it even harder for software to segment and recognize individual characters, while delayed strokes (like diacritic marks) offer more opportunity for confusion. To illustrate the challenges involved: good handwriting recognition software should be able to distinguish a single Chinese character from over 30,000 possible ideograms. Factors such as the age of the writer, their handedness, their country of origin and even the writing surface can impact the writing they produce – and that’s before considering the effects of different languages and alphabets. Handwriting recognition poses significant technical challenges due to the huge variability of handwriting styles. These ‘training samples’ (as they’re known in AI research) are always treated with the greatest regard for privacy and security – and are a huge asset to the company, helping us to refine and enhance our technology. Much of our work is made possible by anonymized sample data shared with us voluntarily by users from around the world. From them, they build language vocabularies, elaborate models to predict the next character in a sentence, and design systems identifying and correcting misspellings. The team uses textual corpora containing hundreds of millions of words obtained from publicly available documents and articles. Our Natural Language Processing (NLP) team develops algorithms that can understand languages as naturally as any human being. They employ graph-based techniques to enable recognition, with real-time processing a major challenge. They address problems that can’t be solved by seq2seq approaches, like the recognition of mathematical expressions, musical notation or diagrams and charts. This team builds mathematical models based on bidimensional parsers and/or grammars. The techniques must be adapted to different alphabets and conventions, to enable the recognition of, for example, right-to-left languages like Arabic or Hebrew, diacritic vowels in Indian scripts, Chinese ideograms, the Korean Hangul alphabet or vertically sequenced Japanese Hiragana, Katakana or Kanji characters. ![]() ![]() Our Text Handwriting Research team uses machine learning techniques to solve problems that can be formulated as sequence-to-sequence (or seq2seq) conversion problems – such as when converting handwritten text into its constituent characters. ![]() ![]() To build the world’s most accurate handwriting recognition engine, we’ve conducted (and continue to conduct) ongoing research into the minutiae of language formation: how sentences are constructed from words and words from characters how diacritic marks are placed above or below certain vowels, and so on.Īt MyScript, several groups of researchers collaborate to create and evolve a best-in-class system that’s capable of understanding a remarkable range of handwritten content. Our tech is backed by over 20 years of research and development. We use AI to interpret handwritten content in over 70 languages, to analyze the structure of handwritten notes, to understand mathematical equations and even to recognize and convert hand-drawn musical notation. Our core software products are powered by proprietary It refers to the field of computer science involved in the creation of intelligent machines that can reproduce and enhance certain capabilities of the human brain – such as reading, understanding or analysis. AI is short for ‘artificial intelligence’.
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