We propose a new method for o!-line recognition of unconstrained handwritten words consisting of Korean and numeric characters. To overcome the di$culty in separating touching characters, we adopt an over-segmentation strategy. Given a slice of the input word image, we "nd the optimal segment combination using a lexicon-driven word scoring technique and a nearest-neighbor classi"er. The optimal...

In this paper, multi-class classification system is of handwritten Arabic words using Dynamic Bayesian Network (DBN) is proposed, in which technical details are presented in terms of three stages, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, features are extracted from each normalized word, where...

Off-line handwritten word/phrase recognition systems generally have monotonically cascaded architecture through several processing steps. In these architectures, the recognition engine follows a static model with a fixed feature space. Built-in resources are exhaustively used at each stage of the serial engine regardless of input complexity. However, the perception of a word is fundamentally an...

This paper presents a multiple classifier system applied to the handwritten word recognition (HWR) problem. The goal is to analyse the influence of different global classifiers taken isolatedly as well as combined in a particular HWR task. The application proposed is the recognition of the Portuguese handwritten names of the months. The strategy takes advantage of the complementary mechanisms o...

This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is in...

No satisfactory solutions are yet available for the offline recognition of handwritten cursive words, including the words of Arabic text. Word matching algorithms can greatly improve the OCR output when recognizing words of known and limited vocabulary. This paper describes the word matching algorithm used in the JU-OCR2 optical character recognition system of handwritten Arabic words. This sys...

Handwritten word recognition and spotting of low-resource scripts are difficult as sufficient training data is not available and it is often expensive for collecting data of such scripts. This paper presents a novel cross language platform for handwritten word recognition and spotting for such low-resource scripts where training is performed with a sufficiently large dataset of an available scr...

Expert readers exhibit a remarkable ability to recognize handwriting, in spite of enormous variability in character shape-a competence whose cerebral underpinnings are unknown. Subliminal priming, combined with neuroimaging, can reveal which brain areas automatically compute an invariant representation of visual stimuli. Here, we used behavioral and fMRI priming to study the areas involved in i...

This paper describes an interpretation system for on-line cursive handwriting that requires very little initial training and that rapidly learns, and thus adapts to, the handwriting style of a user. Key features are a shape analysis algorithm that eeciently determines shapes in the handwritten word, a linear segmentation algorithm that optimally matches characters identiied in the handwritten w...

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-...