ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly model...

W e propose a novel user-dependent method for the recognition of on-line handwritten notes. The method employs as a dissimilarity measure the “degree of morphing” between an input curve and a template cuhe . A physicsbased approach substantiates the “degree of morphing” as a deformation energy and casts the pfoblem as an energy minimization problem. The method operates upon key segmentation poi...

This report describes analogic algorithms used in the preprocessing and segmentation phase of off-line handwriting recognition tasks. A segmentation based handwriting recognition approach is discussed i.e. the system attempts to segment the words into their constituent letters. In order to improve their speed, the utilized CNN algorithms, whenever possible, use dynamic, wave front propagationba...

We propose a novel user-dependent method for the recognition of on-line handwritten notes. The method employs as a dissimilarity measure the “degree of morphing” between an input curve and a template cutie. A physics-based approach substantiates the “degree of morphing” as a deformation energy and casts the pr’oblem as an energy minimization problem. The method operates upon key segmentation po...

Graph-based methods have been widely used by the document image analysis and recognition community, as different objects content in images is best represented this powerful structural representation. Designing of novel computation tools for processing these graph-based representations has always remained a hot topic research. Recently, Graph Neural Network (GNN) solving problems domain recognit...

Cursive character recognition is a challenging task due to high variability and intrinsic ambiguity of cursive letters. This paper presents C-Cube (Cursive Character Challenge), a new public-domain cursive character database. C-Cube contains 57293 cursive characters manually extracted from cursive handwritten words, including both upper and lower case versions of each letter. The database can b...

Handwritten characters are seen everywhere in our day-to-day life. Almost all the things we do involve letters, from writing cheques to notes manually. character recognition is considered as a core diversity of emerging application by using concepts machine learning. It used widely for performing practical applications such reading computerized bank cheques. However, executing system carry out ...

In this paper, a new proposed system for Persian printed numeral characters recognition with emphasis on representation and recognition stages is introduced. For the first time, in Persian optical character recognition, geometrical central moments as character image descriptor and fuzzy min-max neural network for Persian numeral character recognition has been used. Set of different experiments ...

Among the many handwritten character recognition algorithms that have been proposed in the past few years, few of them use models which are able to simulate handwriting. This can be explained by the fact that simulation models require the estimation of strokes starting from statistic images of letters, while crossing and overlapping strokes make this estimation di$cult. The approach we suggest ...