STATE is a multimodal tool for document processing and text transcription. Its graphical front-end can be easily connected to different text recognition back-ends. New features and improvements are presented in this work: the interactive correction of one word in the transcribed line has been improved to reestimate the entire transcription line using the user feedback and speech input has been integrated in the multimodal interface enabling the user to also utter the word to be corrected, giving the user the possibility to use the interface according to her preferences or the task at hand. Thus, at the current version of STATE, the user can type, write on the screen with a stylus, or utter the incorrectly recognized word, and then, the system uses the user feedback in any of the proposed modalities to reestimate the transcribed line so as to hopefully correct other errors which could be caused by the mistaken word the user has corrected.

Our open source real-time recognition engine for on-line isolated handwritten characters is a 3-Nearest Neighbor classifier that uses approximate dynamic time warping comparisons with a set of prototypes filtered by two fast distance-based methods. This engine achieved excellent classification rates on two writer-independent tasks:UJIpenchars and Pendigits. We present the integration of multilayer perceptrons into our engine, an improvement that speeds up the recognition process by taking advantage of the independence of these networks’ classification times from training set sizes. We also present experimental results on our new publicly available UJIpenchars2 database and on Pendigits.

State is an interactive system for ancient and handwritten document transcription with several input modalities for entering and correcting text. It has a flexible architecture that allows easy connection to different OCR systems.

We present a complete assisted transcription system for ancient documents: State. The system consists of two applications: a pen-based, interactive application to assist humans in transcribing ancient documents and a recognition engine which offers automatic transcriptions via a web service. The interaction model and the recognition algorithm employed in the current version of State are presented. Some preliminary experiments show the productivity gains obtained with the system when transcribing a document and the error rate of the current recognition engine.

This article shows the way a speech recognition module has been implemented that provides the translation between voice input and the convenient text based commands to be executed on an already existing web robot. The novel contribution is the way the procedure is defined to be run over the Internet, and the interface implemented to connect any kind of external speech recognition program to the robot controller. First section gives an overview to the whole project, from a user point of view. Secondly, the software architecture is presented, giving low level details on the connectivity procedure and the interface defined between the remote controller and the speech recognition and synthesizer program. Moreover, it introduces some of the technologies (e.g. Microsoft Speech SDK) used to implement this capability. Finally, results are shown by means of both, accuracy details of the speech recognition procedure, and computing time.

A new algorithm to compute the edit distance between cyclic strings is presented. Experimental results with synthetic cyclic strings and a handwritten digits recognition task show that the new algorithm is faster than Maes' (1990) and Gregor and Thomason's (1993) algorithms.

This paper extends previous work exploring the use of Subsequential Transducers to perform speech-input translation in limited-domain tasks. This is done following an integrated approach in which a Subsequential Transducer replaces the input-language model of a conventional speech recognition system, and is used both as language and translation model. This way, the search for the recognised sentence also produces the corresponding translation. A corpus-based approach is adopted in order to build the required models from training data. Experimental results are presented for the translation task considered in the EUTRANS project: one in the hotel domain with more than 500 words per language and language perplexities near to 10.

The normalized edit distance (NED) between two strings X and Y is defined as the minimum quotient between the sum of weights of the edit operations required to transform X into Y and the length of the editing path corresponding to these operations. An algorithm for computing the NED was introduced by Marzal and Vidal (1993) that exhibits 0(mn/sup 2/) computing complexity, where m and n are the lengths of X and Y. We propose here an algorithm that is observed to require in practice the same 0(mn) computing resources as the conventional unnormalized edit distance algorithm does. The performance of this algorithm is illustrated through computational experiments with synthetic data, as well as with real data consisting of OCR chain-coded strings.>

Given two strings X and Y over a finite alphabet, the normalized edit distance between X and Y, d(X,Y) is defined as the minimum of W(P)/L(P), where P is an editing path between X and Y, W(P) is the sum of the weights of the elementary edit operations of P, and L(P) is the number of these operations (length of P). It is shown that in general, d(X,Y) cannot be computed by first obtaining the conventional (unnormalized) edit distance between X and Y and then normalizing this value by the length of the corresponding editing path. In order to compute normalized edit distances, an algorithm that can be implemented to work in O(m*n/sup 2/) time and O(n/sup 2/) memory space is proposed, where m and n are the lengths of the strings under consideration, and m>or=n. Experiments in hand-written digit recognition are presented, revealing that the normalized edit distance consistently provides better results than both unnormalized or post-normalized classical edit distances.>

A new N-best sentence hypotheses enumeration algorithm for continuous speech recognition that correctly incorporates duration constraints and which is very efficient in practice is presented. Based on the most general two-level procedure, this algorithm provides greater flexibility in acoustic modeling than previous approaches and is very well suited for large vocabulary applications.>