Jin Hyung Kim

CROHME2011: Competition on Recognition of Online Handwritten Mathematical Expressions

A competition on recognition of online handwritten mathematical expressions is organized. Recognition of mathematical expressions has been an attractive problem for the pattern recognition community because of the presence of enormous uncertainties and ambiguities as encountered during parsing of the two-dimensional structure of expressions. The goal of this competition is to bring out a state of the art for the related research. Three labs come together to organize the event and six other research groups participated the competition. The competition defines a standard format for presenting information, provides a training set of 921 expressions and supplies the underlying grammar for understanding the content of the training data. Participants were invited to submit their recognizers which were tested with a new set of 348 expressions. Systems are evaluated based on four different aspects of the recognition problem. However, the final rating of the systems is done based on their correct expression recognition accuracies. The best expression level recognition accuracy (on the test data) shown by the competing systems is 19.83% whereas a baseline system developed by one of the organizing groups reports an accuracy 22.41% on the same data set.

Utilization of hierarchical, stochastic relationship modeling for Hangul character recognition

In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by which strokes as well as relationships are stochastically represented by utilizing the hierarchical characteristics of target characters. A character is defined by a multivariate random variable over the components and its probability distribution is learned from a training data set. To overcome difficulties of the learning due to the high order of the probability distribution (a problem of curse of dimensionality), the probability distribution is factorized and approximated by a set of lower-order probability distributions by applying the idea of relationship decomposition recursively to components and subcomponents. Based on the proposed method, a handwritten Hangul (Korean) character recognition system is developed. Recognition experiments conducted on a public database show the effectiveness of the proposed relationship modeling. The recognition accuracy increased by 5.5 percent in comparison to the most successful system ever reported.

ICFHR 2012 Competition on Recognition of On-Line Mathematical Expressions (CROHME 2012)

This paper presents an overview of the second Competition on Recognition of Online Handwritten Mathematical Expressions, CROHME 2012. The objective of the contest is to identify current advances in mathematical expression recognition using common evaluation performance measures and datasets. This paper describes the contest details including the evaluation measures used as well as the performance of the 7 submitted systems along with a short description of each system. Progress as compared to the 1st version of CROHME is also documented.

Bayesian network modeling of Hangul characters for online handwriting recognition

In this paper we propose a Bayesian network framework for explicitly modeling components and their relationships of Korean Hangul characters. A Hangul character is modeled with hierarchical components: a syllable model, grapheme models, stroke models and point models. Each model is constructed with subcomponents and their relationships except a point model, the primitive one, which is represented by a 2D Gaussian for X-Y coordinates of a point instances. Relationships between components are modeled with their positional dependencies. For online handwritten Hangul characters, the proposed system shows higher recognition rates than the HMM system with chain code features: 95.7% vs. 92.9% on average.

An example-based prior model for text image super-resolution

This paper presents a prior model for text image super-resolution in the Bayesian framework. In contrast to generic image super-resolution task, super-resolution of text images can be benefited from strong prior knowledge of the image class: firstly, low-resolution images are assumed to be generated from a high-resolution image by a sort of degradation which can be grasped through example pairs of the original and the corresponding degradation; secondly, text images are composed of two homogeneous regions, text and background regions. These properties were represented in a Markov random field (MRF) framework. Experiments showed that our model is more appropriate to text image super-resolution than the other prior models.

Handwritten Hangul character recognition with hierarchical stochastic character representation

In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. In this paper, we propose a stochastic modeling scheme by which strokes as well as relationships are represented by utilizing the hierarchical characteristics of target characters. Based on the proposed scheme, a handwritten Hangul (Korean) character recognition system is developed. The effectiveness of the proposed scheme is shown through experimental results conducted on a public database.

Top-Down Search with Bottom-Up Evidence for Recognizing Handwritten Mathematical Expressions

In handwritten mathematical expressions (ME), understanding the general structure of an ME is often easier than resolving local ambiguities. For instance, identifying a key operator in terms of its spatial relationship with its subordinates is relatively easier than resolving the ambiguities of single symbol identity and local spatial relationships. In addition, decisions related to key operators often occur close to the top (root) of the parse tree, while local decisions take place at the bottom of it. Based on these observations, we propose an incremental search framework in which a parse tree is expanded by tentatively selecting the key operators of an expression. The goodness of the selection is defined by the likelihood of key symbol, the goodness of the sub expressions, and their spatial relationships. In this framework, ambiguous local parts are processed after tentative decisions have been made at the global level. To handle explosiveness of key operator selection, an admissible heuristic function is defined based on the direct relationship of the key operator with the symbols at the bottom level. An experimental evaluation shows that our system is promising. Using it a robust interpretation can be made by utilizing global information and an interpretation can be reached quickly by the admissible heuristic function.

Learning the lexicon from raw texts for open-vocabulary Korean word recognition

In this paper, we propose a novel method of building a language model for open-vocabulary Korean word recognition. Due to the complex morphology of Korean, it is inappropriate to use lexicons based on the linguistic entities such as words and morphemes in open-vocabulary domains. Instead, we build the lexicon by collecting variable length character sequences from the raw texts using a dynamic Bayesian network model of the language. In simulated word recognition experiments, the proposed language model could find correct words from lattices of character candidates in 94.3% of cases, increasing the word recognition rates by 20.9%.