https://www.cs.ubc.ca/~murphyk/Teaching/CS540-Spring10/projects/Yu-hsmm09.pdf

Hidden semi-Markov models

Advances in neural information processing systems 3

A variety of different scripts are used in writing languages throughout the world. In a multiscript, multilingual environment, it is essential to know the script used in writing a document before an appropriate character recognition and document analysis algorithm can be chosen. In view of this, several methods for automatic script identification have been developed so far. They mainly belong to two broad categories-structure-based and visual-appearance-based techniques. This survey report gives an overview of the different script identification methodologies under each of these categories. Methods for script identification in online data and video-texts are also presented. It is noted that the research in this field is relatively thin and still more research is to be done, particularly in the case of handwritten documents.

/pdf/script-recognition-a-review-3mbwqe8eyw.pdf

Script Recognition—A Review

Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-model-based offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

/pdf/markov-models-for-offline-handwriting-recognition-a-survey-4ijdjn6mf0.pdf

Markov models for offline handwriting recognition: a survey

Off-line signature verification based on grey level information using texture features

An incremental and growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. We propose a new algorithm for a SOM which can learn new input data (plasticity) without degrading the previously trained network and forgetting the old input data (stability). We report the validation of this model on experiments using a synthetic problem, the IRIS database and the handwriting digit recognition problem over a portion of the NIST database. Finally we show how to use this network for clustering and semi-supervised clustering.

An incremental growing neural gas learns topologies

Text-independent writer recognition using multi-script handwritten texts

Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic informations we make a sampling operation by the consideration of trajectory curvatures. We proceed to calculate the curvilinear velocity signal and use the beta-elliptical modelling which is developed in on-line systems to calculate other characteristics. Our approach is validated by Hmm Tool Kit (HTK) recognition system using IFN/ENIT database.

http://www.cvc.uab.es/icdar2009/papers/3725a411.pdf

Arabic Handwriting Recognition Using Restored Stroke Chronology

We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the HMM framework can significantly improve the discriminating capacity of the HMMs to deal with very difficult pattern recognition tasks such as unconstrained Arabic handwriting recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss, and Poisson) for the explicit state duration modeling have been used, and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.

/pdf/arabic-handwritten-word-recognition-using-hmms-with-explicit-m1oaf2ru69.pdf

Abdellatif Ennaji

Papers

An incremental growing neural gas learns topologies

Text-independent writer recognition using multi-script handwritten texts

Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition

Arabic Handwriting Recognition Using Restored Stroke Chronology

Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration