A study on the use of Gabor features for Chinese OCR

The authors revisit the topic of Gabor feature extraction for Chinese OCR. We adopt a very simple discriminant function to construct a maximum discriminant function based character recognizer. We experiment with a simple way of forming a feature vector for each character image by extracting Gabor features using one wavelength at locations uniformly sampled with one spatial resolution. Extensive experiments on large vocabulary Chinese OCR for both machine-printed and handwritten characters are performed by using a large amount of training and testing data to demonstrate the effectiveness of the Gabor features for Chinese OCR. Using Gabor features as raw features, we have constructed several state-of-the-art Chinese OCR engines.published_or_final_versio

Feature Extraction Methods for Character Recognition

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A Study On the Use of 8-Directional Features For Online Handwritten Chinese Character Recognition

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Subword-based Stochastic Segment Modeling for Offline Arabic Handwriting Recognition

In this paper, we describe several experiments in which we use a stochastic segment model (SSM) to improve offline handwriting recognition (OHR) performance. We use the SSM to re-rank (re-score) multiple decoder hypotheses. Then, a probabilistic multi-class SVM is trained to model stochastic segments obtained from force aligning transcriptions with the underlying image. We extract multiple features from the stochastic segments that are sensitive to larger context span to train the SVM. Our experiments show that using confidence scores from the trained SVM within the SSM framework can significantly improve OHR performance. We also show that OHR performance can be improved by using a combination of character-based and parts-of-Arabic-words (PAW)-based SSMs

Facial Landmark Detection Using Affine Graph Matching and a Genetic Search Algorithm

This paper proposes a method that finds landmark points on the face, which is one of the main tasks in a face recognition system. Salient facial landmark detection is important because it enables face normalization and leads to size and orientation invariant face recognition. The presented approach is based on an affine graph matching technique and uses a genetic algorithm to perform the search. The feasibility of our methodology for detection tasks related to face landmark point detection has been deployed using the ORL face image database. Experiments show satisfactory results under relatively wide conditions. The GA searching approach is essential because it effectively searches the solution space.

Empirical mode decomposition-based facial pose estimation inside video sequences

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions