Text-Independent Online Writer Identification Using Hidden Markov Models

Abstract

SUMMARY In text-independent online writer identification, the Gaussian Mixture Model(GMM) writer model trained with the GMM-Universal Background Model(GMM-UBM) framework has acquired excellent performance. However, the system assumes the items in the observation sequence are independent, which neglects the dynamic information between observations. This work shows that although in the text-independent application, the dynamic information between observations is still important for writer identification. In order to extend the GMM-UBM system to use the dynamic information, the hidden Markov model(HMM) with Gaussian observation model is used to model each writer's handwriting in this paper and a new training schematic is proposed. In particular, the observation model parameters of the writer specific HMM are set with the Gaussian component parameters of the GMM writer model trained with the GMM-UBM framework and the state transition matrix parameters are learned from the writer specific data. Experiments show that incorporating the dynamic information is capable of improving the performance of the GMM-based system and the proposed training method is effective for learning the HMM writer model.

Classification and Extraction of Appositives based on English and Chinese Bilingual Corpus

“…The statistical method used in this paper is based on Hidden Markov Models [6] to recognize the part-of-speech of appositives. As a simple and effective statistical tool, Hidden Markov Models has been widely used in many fields, such as natural language processing [7,8], speech recognition [9,10] and bioinformatics [11,12]. Compared with such statistical methods as Hidden Markov Models, the traditional rule-based part-of-speech analysis usually has the following disadvantages:…”

Sequential Authentication Using Handwriting Biometrics for Free Response e-Testing

“…But, this method [3] could not apply to numeric characters that contain curved strokes due to optimize for Japanese kanji (Chinese character). Wu et al [4] proposed writer identification incorporating the time information by hidden Markov model to improve the perfor-Manuscript received August 1, 2019. Manuscript publicized January 20, 2020.…”

“…Kutzner et al [5] proposed writer authentication using various geometrical, statistical and dynamic features in handwritten cursive texts and single character words. These method [4], [5] has high accuracy with the dynamic information of pen coordinate, but we assumed the pen angle can be taken for prevent to forged writing such as a text made by tracing or copy. In addition, these researches [3]- [5] assume to identify users at the end of the character or text input and calculate the likelihood of being a principle from all of a subdivided character such as stroke unit.…”