We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to bootstrap a few candidates for a final evaluation phase.

This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Several experiments have been performed using both single and multiple writer data. Lexica of variable size (from 10,000 to 50,000 words) have been used. The use of language models is shown to improve the accuracy of the system (when the lexicon contains 50,000 words, error rate is reduced by ∼50 % for single writer data and by ∼25 % for multiple writer data). Our approach is described in detail and compared with other methods presented in the literature to deal with the same problem. An experimental setup to correctly deal with unconstrained text recognition is proposed. Models.

Introduction There are many single author historical handwritten manuscripts which would be useful to index and search. Examples of these large archives are the papers of George Washington, Margaret Sanger and W. E. B Dubois. Currently, much of this work is done This material is based on work supported in part by the National Science Foundation, Library of Congress and Department of Commerce under cooperative agreement number EEC9209623, in part by the United States Patent and Trademarks Office and the Defense Advanced Research Projects Agency/ITO under ARPA order number D468, issued by ESC/AXS contract number F19628-95-C-0235, in part by NSF IRI-9619117 and in part by NSF Multimedia CDA-9502639. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors. manually. For example, 50,000 pages of Margaret Sanger's work were recently indexed and placed on a CDROM. A pa

A system for off-line cursive script recognition is presented. A new normalization technique (based on statistical methods) to compensate for the variability of writing style is described. The key problem of segmentation is avoided by applying a sliding window on the handwritten words. A feature vector is extracted from each frame isolated by the window. The feature vectors are used as observations in letter-oriented continuous density HMMs that perform the recognition. Feature extraction and modeling techniques are illustrated. In order to allow the comparison of the results, the system has been trained and tested using the same data and experimental conditions as in other published works. The performance of the system is evaluated in terms of character and word (with and without lexicon) recognition rate. Results comparable to those of more complex systems have been achieved.

This paper presents new techniques for slant and slope removal in cursive handwritten words. Both new methods make no use of heuristics and no manual parameter tuning is needed. This avoids the heavy experimental effort required to find the optimal configuration of a parameter set. A comparison between the new deslanting technique and the method proposed by Bozinovic and Srihari was made by measuring the performance of a word recognition system on different databases. The new technique is shown to improve the recognition rate of the system and to avoid the long exploration of the parameter space needed by the other method.

This paper review the state of the art in o#-line Roman cursive han dw iting recognition. The input provided to an o#-line han iting recognition system is an image of a digit, aw ord, or - more generally - some text, and the system produces, as output, an ASCII transcription of the input. This taskinvolves a number of processing steps, some of w ich are quite di#cult. Typically, preprocessing, normalization, feature extraction, classification, and postprocessing operations are required. We'll survey the state of the art, analyze recent trends, and try to identify challenges for future research in this field.

This paper presents a system for the offline recognition of cursive handwritten lines of text. The system is based on continuous density HMMs and Statistical Language Models. The system recognizes data produced by a single writer. No a-priori knowledge is used about the content of the text to be recognized. Changes in the experimental setup with respect to the recognition of single words are highlighted. The results show a recognition rate of #85% with a lexicon containing 50'000 words. The experiments were performed over a publicly available database.

This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: the first one is a feature extractor, the second one an LVQ. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49000 isolated characters.

A holistic system for the recognition of handwritten Farsi/Arabic words using right}left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), is used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results. � 2001 Pattern

As whiteboards have become a popular tool in meeting rooms, there has been a growing interest in making use of the whiteboard as a user interface for human computer interaction. Therefore, systems based on electronic whiteboards have been developed in order to serve as meeting assistants for e.g. collaborative working. However, as special pens and erasers are required, the natural interaction is restricted. In order to render this communication method more natural it was proposed to retain ordinary whiteboard and pens and to visually observe the writing process using a video camera [11, 9]. In this paper a prototype system for automatic video-based whiteboard reading is presented. The system is designed for recognizing unconstrained handwritten text and is further characterized by an incremental processing strategy in order to facilitate recognizing portions of text as soon as they have been written on the board. We will present the methods employed for extracting text regions, pre-processing, feature extraction, and statistical modeling and recognition. Evaluation results on a writer independent unconstrained handwriting recognition task demonstrate the feasibility of the proposed approach.