In document image analysis (DIA) especially in handwritten document recognition, standard databases play signi ̄cant roles for evaluating performances of algorithms and comparing results obtained by di®erent groups of researchers. The ̄eld of DIA regard to Indo-Persian documents is still at its infancy compared to Latin script-based documents; as such standard datasets are not still available in ...

Skew detection and correction (SDC) has a direct effect in efficiency and exactitude of documents’ segmentation and analysis and thus is considered as a very important step in documents’ analysis field. Skew is a major problem in documents’ analysis for every language. For Arabic/Persian document scripts this problem is more severe because of special features of these languages. In this paper a...

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-...

This paper discusses a methodology for handwritten character recognition applying feature subset selection to reduce number of features. Its novelty lies in the use of a genetic algorithm for the preparation of input data for a support vector machine which is employed to recognize the handwritten Persian digits in particular. Comprehensive experiments on handwritten Persian digits demonstrate t...

Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...

Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemble method. Furthermore, the feature selection is used to reduce the costs of ...

Persian handwritten numerals recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten numerals is a difficult task owing to various writing styles of individuals. A robust and efficient method for Persian/Arabic handwritten numerals recognition based on K Nearest Neighbors (K-NN) classifier is presented in this paper. The sy...

This paper discusses the use of fast and customized Dynamic Time Warping method for offline Persian handwriting recognition that could be easily extended to Arabic language. Many systems in this field are based on either Neural Network or Hidden Markov Model that suffer from low recognition rate, sensitivity to noises or wide range of parameters that reduce system performance. The complete syst...

Recognition of handwritten numbers in any language is one of the most important issues attracted many researchers and this is because of the increasing importance of the issue in today's world and in applications such as automatic detection of mail addresses or identification of numbers of bank checks. Given the great interclass diversities and much extra-class similarities between some Persian...