A key to success to contextual in-video advertising is finding advertising keywords on video contents effectively, but there has been little literature in the area so far. This paper presents some preliminary results of our learning-based system that finds relevant advertising keywords on particular scene of video contents using their scripts. The system is trained with not only features proven useful in earlier studies but novel features that reflect the situation of a targeted scene. Experimental results show that the new features are potentially helpful for enhancing the accuracy of keyword extraction for contextual in-video advertising.

In this paper we consider a hidden Markov mesh random field (HMMRF) for character recognition. The model consists of a "hidden" Markov mesh random field (MMRF) and an overlying probabilistic observation function of the MMRF. Just like the 1-D HMM, the hidden layer is characterized by the initial and the transition probability distributions, and the observation layer is defined by distribution functions for vector-quantized (VQ) observations. The HMMRF-based method consists of two phases: decoding and training. The decoding and the training algorithms are developed using dynamic programming and maximum likelihood estimation methods. To accelerate the computation in both phases, we employed a look-ahead scheme based on maximum marginal it a posteriori probability criterion for third-order HMMRF. Tested on a larget-set handwritten Korean Hangul character database, the model showed a promising result: up to 87.2% recognition rate with 8 state HMMRF and 128 VQ levels.

In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multi-lingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC.

We propose a new methodology for off-line handwritten character recognition using a 2D hidden Markov mesh random field (HMMRF)-based statistical approach. In the HMMRF model for character recognition, the inputs to the model are assumed to be sequences of discrete symbols chosen from a finite alphabet. In the proposed methodology, the grey-level input image is first divided into nonoverlapping blocks with same size. Then, each block is encoded into a discrete symbol based on the local features of the block by using the vector quantizer. The HMMRF-based statistical approach necessitates two phases: the decoding phase and the training phase. In both phases we use the lookahead scheme based on a maximum, marginal a posteriori probability criterion for a third-order HMMRF model. In order to verify the performance of the proposed methodology for off-line handwritten character recognition, a large-set handwritten Hangul database was used. Experimental results revealed the viability of the HMMRF-based statistical approach on the task of off-line handwritten character recognition.

Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touched or overlapped characters may be lost in many cases. If we analyze gray-scale images, however, specific topographic features and the variation of intensities can be observed in the character boundaries. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted from the gray-scale images. Then a nonlinear character segmentation path in each character segmentation region is found by using multi-stage graph search algorithm. Finally, in order to confirm the nonlinear character segmentation paths and recognition results, a recognition-based segmentation method is adopted. Through the experiments with various kinds of printed documents, it is convinced that the proposed methodology is very effective for the segmentation and recognition of touched and overlapped characters.

In recent years, there have been some attempts to extend one-dimensional hidden Markov model (HMM) to two-dimensions. This paper presents a new statistical model for image modeling and recognition under the assumption that images can be represented by a third-order hidden Markov mesh random field (HMMRF) model. We focus on two major problems: image decoding and parameter estimation. A solution to these problems is derived from the scheme based on a maximum, marginal a posteriori probability criterion for the third-order HMMRF model. We also attempt to illustrate how theoretical results of HMMRF models can be applied to the problems of handwritten character recognition.

An efficient recognition scheme for large-set handwritten characters is proposed in the framework of multiple stochastic models, in this case, first order hidden Markov models which can model stochastically the input pattern with numerous variations. In this scheme, after extracting four kinds of regional projection contours for an input pattern by using the regional projection contour transformation, four kinds of HMMs are constructed during the training phase based on the direction components of these contours. In the recognition phase, the four kinds of HMMs constructed in the training phase are combined to output the final recognition result for an input pattern.>