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.

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.

Contents 1 Introduction 5 2 State of the art 7 2.2 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Structure of a CWR System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.1 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.2 The segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.3 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.4 Lexicon reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.5 The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.6 The recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.7 Human Reading Inspired Systems . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.8 Holistic approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This paper presents a system for the oine recognition of cursive handwritten lines of text. The system is based on continuous density HMMs and Statistical Language Models.