Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance. as in reading handwritten notes in a PDA. in postal addresses on envelopes, in amounts in bank checks, in handwrilten fields in forms,...

USING STATISTICAL METHODS Krishna S. Nathan, Homayoon S. M. Beigi, Jayashree Subrahmonia, Gregory J. Clary and Hiroshi Maruyama Handwriting Recognition Group IBM Research, T.J. Watson Center, Yorktown Heights, NY 10598 ABSTRACT We address the problem of automatic recognition of unconstrained handwritten text. Statistical methods, such as hidden Markov models (HMMs) have been used successfully f...

In domain of analytic cursive word recognition, there are two main approaches: explicit segmentation based and implicit segmentation based. However, both approaches have their own shortcomings. To overcome individual weaknesses, this paper presents a hybrid strategy for recognition of strings of characters (words or numerals). In a two stage dynamic programming based, lexicon driven approach, f...

The segmentation of individual words into characters is a vital process in handwritten character recognition systems. In this paper, a novel approach is proposed to segment handwritten Arabic text (words). We consider the “Naskh” font style. The segmentation algorithm employs seven agents in order to detect regions where segmentation is illegal. Feature points (end points) are extracted from th...

We introduce a new method for size and orientation normalization of unconstrained handwritten words based on the Hough transform. A modi ed Hough transform is applied to extremum points along the y coordinate to extract parallel lines corresponding to the boundary lines separating di erent vertical zones of the handwritten word. One dimensional Gaussian smoothing with variable variance is appli...

This paper proposes a novel language model based combination method for ensembles of offline handwritten text line recognisers. The individual recognisers are based on hidden Markov models and the ensembles are generated with the bagging method. The proposed combination method extends the ROVER framework by rescoring the word transition networks with a language model. Experiments conducted on a...

An alignment method based on the Viterbi algorithm is proposed to find mappings between word images of a given handwritten document and their respective (ASCII) words on its transcription. The approach takes advantage of the underlying segmentation made by Viterbi decoding in handwritten text recognition based on Hidden Markov Models (HMMs). Two HMMs modelling schemes are evaluated: one using 7...

This paper deals with the problem of o3-line handwritten text recognition. It presents a system of text recognition that exploits an original principle of adaptation to the handwriting to be recognized. The adaptation principle is based on the automatic learning, during the recognition, of the graphical characteristics of the handwriting. This on-line adaptation of the recognition system relies...

This communication investigates the automatic reading of unconstrained omni-writer handwritten texts. It shows how to endow the reading system with learning faculties necessary to adapt the recognition to each writer’s handwriting. In the first part of this communication, we explain how the recognition system can be adapted to a current handwriting by exploiting the graphical context defined by...

Analysis of allographs (characters) and allograph combinations (words) is the key for obtaining the discriminating elements of handwriting. While allographs usually inhabit in words and segregation of a word into allographs is more subjective than objective, especially for cursive writing, analysis of handwritten words is a natural and better option. In this study, a handwritten word image is c...