On-line Handwritten Mathematical Expression Recognition Method Based on Statistical and Semantic Analysis

Abstract

Recognition of handwritten mathematical expressions (HMEs) has become a cutting edge research topic recently, as there are increasingly needs for pen-inputting applications. In this paper, we presented a novel framework to analyse HME layout and semantic information. This framework includes three steps, namely symbol segmentation, symbol recognition and semantic relationship analysis. For symbol segmentation, a decomposition on strokes is operated, then dynamic programming is adopted to find the paths corresponding to the best segmentation manner and reduce the stroke searching complexity. For symbol recognition, spatial geometry and directional element features are classified by a Gaussian Mixture Model learnt through Expectation-Maximization algorithm. At last, in the semantic relationship analysis module, a ternary tree is utilized to to store the ranked symbols through calculating the operator priorities. The motivation for our work comes from the apparent difference in writing styles across western and Chinese populations. Our results are reasonable and show promise on the private dataset.

Robust encoder-decoder learning framework for offline handwritten mathematical expression recognition based on a multi-scale deep neural network

“…Amit et al [8] proposed a new method using multiple structure recognition algorithms. In recognition of handwritten mathematical expressions, the identification method proposed by Hu et al [9] is based on the segmentation of handwritten strokes. Zhang et al [10] proposed a model using Watch, Attend, and Parse to perform image recognition that includes mathematical expressions.…”

Recognizing Offline Handwritten Mathematical Expressions (ME) based on a Predictive Approach of Segmentation using K-NN Classification

“…Handwritten Mathematical Expression (ME) recognition has been a topic of intensive research in recent years due to its large number of applications A significant number of researchers have attempted to resolve the problem of ME recognition (Chan et al, 2000;Plamondon, 2000;Tapia & Rojas, 2007;Hu et al, 2014). The recognition of offline handwritten mathematical expressions differs from online handwritten recognition because it requires an explicit segmentation technique.…”