This is the first convolved feature that's fed into the rest of the feed-forward neural network.
Finally, increased internationalization sometimes introduces unexpected collisions: for example, U+4E8C (二), when cursively written, may conflict with the Latin characters “2” and “Z”.Įach hand-input is digested to a 48 x 48 pixel image representing the original character. Collisions and ambiguities between character inventories provided additional complexity.Īs a speedy input tends to drive toward cursive styles, it tends to increase ambiguity, e.g. But the scalability of real-time CNN's for 30-thousand or more characters made this challenge different. The approach Apple employed is similar to what works well for Latin script translators based on MNIST, and where CNN's became the industry standard. Ambiguous handwriting styles, increasing complexity and computational overhead for each n number of strokes per character led Apple researchers to a more "shape driven" approach, agnostic of stroke-order. This obviously complicates large-inventory recognition, as correct character classification tends to get harder with the number of categories to disambiguate.Ī larger pool of characters exposed underlying problems with the stroke-order based approach. While early recognition algorithms mainly relied on structural methods based on individual stroke analysis, the need to achieve stroke-order independence later sparked interest into statistical methods using holistic shape information. Earlier research methods outlined in the article went through an evolution of model approaches over time, with stroke-order playing a significant part in sub-setting the remaining pool of character possibilities into smaller groups with the hopes of improved odds at finding a match. A large corpus of potential character values, variation in handwriting methods, nature and properties of each person's unique hand-writing style makes for a challenging machine learning problem.Ĭonvolutional neural networks are typically used for machine learning problems focused on image recognition and labeling. Several versions of Chinese language sets have been adapted over the years to address variation in frequently used characters over time and geography. The Chinese National Standard GB18030-2005 contains 27,533 entries, making keyboard implementation challenging over the years, so a handwritten translator to codified text is especially valuable in Chinese-speaking populations. This allowed researchers to recognize characters from large sets like GB18030-2005, with only slightly worse accuracy than when using smaller character sets like GB2312-80. The model reportedly performs with asymptotic accuracy degradation as the character pool increases in size.
Apple detailed their real-time machine learning engine for recognizing handwritten Chinese characters, supporting a collection of up to 30,000 characters.
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