Structural Information Implant in a Context Based Segmentation-Free HMM Handwritten Word Recognition System for Latin and Bangla Script

In this paper, an improvement of a 2D stochastic model based handwritten entity recognition system is described. To model the handwriting considered as being a two dimensional signal, a context based, segmentation-free Hidden Markov Model (HMM) recognition system was used. The baseline approach combines a Markov Random Field (MRF) and a HMM so-called Non-Symmetric Half Plane Hidden Markov Model (NSHP-HMM). To improve the results performed by this baseline system operating just on low-level pixel information an extension of the NSHP-HMM is proposed. The mechanism allows to extend the observations of the NSHP-HMM by implanting structural information in the system. At present, the accuracy of the system on the SRTP (Service de Recherche Technique de la Poste) French postal check database is 87.52% while for the handwritten Bangla city names is 86.80%. The gain using this structural information for the SRTP dataset is 1.57%

Automation of Indian Postal Documents written in Bangla and English

International audienceIn this paper, we present a system towards Indian postal automation based on pin-code and city name recognition. Here, at first, using Run Length Smoothing Approach (RLSA), non-text blocks (postal stamp, postal seal, etc.) are detected and using positional information Destination Address Block (DAB) is identified from postal documents. Next, lines and words of the DAB are segmented. In India, the address part of a postal document may be written by combination of two scripts: Latin (English) and a local (State/region) script. It is very difficult to identify the script by which pin-code part is written. To overcome this problem on pin-code part, we have used two-stage artificial neural network based general scheme to recognize pin-code numbers written in any of the two scripts. To identify the script by which a word/city name is written, we propose a water reservoir concept based feature. For recognition of city names, we propose an NSHP-HMM (Non- Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the proposed digit numeral recognition module is 93.14% while that of city name recognition scheme is 86.44%

Use of Markov processes in writing recognition

In this paper, we present a brief survey on the use of different types of Markov models in writing recognition . Recognition is done by a posteriori pattern class probability calculus . This computation implies several terms which, according to the dependency hypotheses akin to the considered application, can be decomposed in elementary conditional probabilities . Under the assumption that the pattern may be modeled as a uni- or two-dimensional stochastic process (random field) presenting Markovian properties, local maximisations of these probabilities result in maximum pattern likelihood . We have studied throughout the article several cases of subpattern probability conditioning. Each case is accompanied by practical illustrations related to the field of writing recognition .Dans cet article, nous présentons une étude sur l'emploi de différents types de modèles de Markov en reconnaissance de l'écriture. La reconnaissance est obtenue par calcul de la probabilité a posteriori de la classe d'une forme. Ce calcul fait intervenir plusieurs termes qui, suivant certaines hypothèses de dépendance liées à l'application traitée, peuvent se décomposer en probabilités conditionnelles élémentaires. Si l'on suppose que la forme suit un processus stochastique uni- ou bidimensionnel qui de plus vérifie les propriétés de Markov, alors la maximisation locale de ces probabilités permet l'atteinte d'un maximum de la vraisemblance de la forme. Nous avons étudié plusieurs cas de conditionnement des probabilités élémentaires des sous-formes. Chaque étude est accompagnée d'illustrations pratiques relatives au domaine de la reconnaissance de l'écriture imprimée et/ou manuscrite