Real time handwriting recognition for mathematic expressions using Hidden Markov Model

Pranoto, Yuliana Melita; Setyati, Endang; Pramana, Edwin; Kristian, Yosi; Budiman, Renato

doi:10.1109/isitia.2016.7828623

Cited by 3 publications

(1 citation statement)

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“…There is another work which recognizes mathematical expression by using HMM and the system was conducted by covering two things: feature modification experiment and code words number experiments. The best result is gained with respect to four combination features [10]. We also notice a system for online Farsi characters recognition.…”

Section: Handcrafted Approachesmentioning

confidence: 82%

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Akouaydi,

Njah,

Ouarda

et al. 2019

Preprint

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Due to the omnipresence of mobile devices, online handwritten scripts have become the most important feeding input to smartphones and tablet devices. To increase online handwriting recognition performance, deeper neural networks have extensively been used. In this context, our paper handles the problem of online handwritten script recognition based on extraction features system and deep approach system for sequences classification. Many solutions have appeared in order to facilitate the recognition of handwriting. Accordingly, we used an existent method and combined with new classifiers in order to get a flexible system. Good results are achieved compared to online characters and words recognition system on Latin and Arabic scripts. The performance of our two proposed systems is assessed by using five databases. Indeed, the recognition rate exceeds 98%.

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Section: Handcrafted Approachesmentioning

confidence: 82%