A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.Comment: preprin

Disentangling Writer and Character Styles for Handwriting Generation

Training machines to synthesize diverse handwritings is an intriguing task. Recently, RNN-based methods have been proposed to generate stylized online Chinese characters. However, these methods mainly focus on capturing a person's overall writing style, neglecting subtle style inconsistencies between characters written by the same person. For example, while a person's handwriting typically exhibits general uniformity (e.g., glyph slant and aspect ratios), there are still small style variations in finer details (e.g., stroke length and curvature) of characters. In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters. Specifically, we present the style-disentangled Transformer (SDT), which employs two complementary contrastive objectives to extract the style commonalities of reference samples and capture the detailed style patterns of each sample, respectively. Extensive experiments on various language scripts demonstrate the effectiveness of SDT. Notably, our empirical findings reveal that the two learned style representations provide information at different frequency magnitudes, underscoring the importance of separate style extraction. Our source code is public at: https://github.com/dailenson/SDT.Comment: accepted by CVPR 2023. Source code: https://github.com/dailenson/SD

Unlocking Self-Teaching: Empowering Especially Dyslexic and Disadvantaged Readers

A number of well-known historical figures taught themselves to read at 2 or 3 years old but some ordinary children were known to do it too and were called natural readers This research identified children who had taught themselves to read in two different reading teaching eras in England to find out how they had developed their initial sound to symbol awareness The methods these children used were investigated through a freeform writing task that showed their level of handwriting skill and knowledge of the language The results were then shared with Reception year teachers in pilot studies The results showed 30 uplift in reading skill school attainment tests SATs In the main study there were 8 teachers and their 175 pupils Spelling and handw riting coordination scales were developed to profile the skills of the group of students from their data on entry to school at 5 years old after 6 months and again on entry to Year 2age 7 years At each stage all the teachers were sent reports on how to help their individual childre