The output of handwritten word recognizers (WR) tends to be very noisy due to various factors. In order to compensate for this behaviour, several choices of the WR must be initially considered. In the case of handwritten sentence/phrase recognition, linguistic constraints may be applied in order to improve the results of the WR. This paper discusses two statistical methods of applying linguisti...

Correct and efficient segmentation of Uyghur words into characters is crucial to the successful recognition. However, little work has been done in this area. There are many connected characters in cursive Uyghur handwriting, which makes the segmentation and recognition of Uyghur words very difficult. To enable large vocabulary Uyghur word recognition using character models, we propose a charact...

This paper presents the IfN’s Offline Handwritten Arabic Word Recognition System. The system uses Hidden Markov Models (HMM) for word recognition, and is based on character recognition without explicit segmentation. The first part of this paper deals with databases for word recognition systems, and in particular, the IFN/ENIT database. The second part gives a short description of the pre-proces...

a r t i c l e i n f o Keywords: Keyword spotting Chinese handwritten documents Word similarity Contextual word model This paper proposes a method for keyword spotting in off-line Chinese handwritten documents using a contextual word model, which measures the similarity between the query word and every candidate word in the document by combining a character classifier and the geometric context a...

This paper investigates rejection strategies for unconstrained offline handwritten text line recognition. The rejection strategies depend on various confidence measures that are based on alternative word sequences. The alternative word sequences are derived from specific integration of a statistical language model in the hidden Markov model based recognition system. Extensive experiments on the...

Handwriting is used to distribute information among people. To access this for further analysis the page needs be optically scanned and converted machine recognizable form. Due unconstrained writing styles along with connected overlapping characters, handwriting recognition remains a challenging task. Most of methods in literature use lexicon-based approaches train their models on large dataset...

The research described in this paper focuses on the presentation of two novel preprocessing techniques for the task of off-line handwritten word recognition. A technique for the identification of straight and skewed underline noise is described along with a novel algorithm for detecting skew in handwritten words. The latter identifies skew by detecting the center of mass in each half of a word ...