Alejandro Héctor Toselli

INTEGRATED HANDWRITING RECOGNITION AND INTERPRETATION USING FINITE-STATE MODELS

The interpretation of handwritten sentences is carried out using a holistic approach in which both text image recognition and the interpretation itself are tightly integrated. Conventional approaches follow a serial, first-recognition then-interpretation scheme which cannot adequately use semantic–pragmatic knowledge to recover from recognition errors. Stochastic finite-sate transducers are shown to be suitable models for this integration, permitting a full exploitation of the final interpretation constraints. Continuous-density hidden Markov models are embedded in the edges of the transducer to account for lexical and morphological constraints. Robustness with respect to stroke vertical variability is achieved by integrating tangent vectors into the emission densities of these models. Experimental results are reported on a syntax-constrained interpretation task which show the effectiveness of the proposed approaches. These results are also shown to be comparatively better than those achieved with other conventional, N-gram-based techniques which do not take advantage of full integration.

Multimodal Interactive Pattern Recognition and Applications

This book presents a different approach to pattern recognition (PR) systems, in which users of a system are involved during the recognition process. This can help to avoid later errors and reduce the costs associated with post-processing. The book also examines a range of advanced multimodal interactions between the machine and the users, including handwriting, speech and gestures. Features: presents an introduction to the fundamental concepts and general PR approaches for multimodal interaction modeling and search (or inference); provides numerous examples and a helpful Glossary; discusses approaches for computer-assisted transcription of handwritten and spoken documents; examines systems for computer-assisted language translation, interactive text generation and parsing, relevance-based image retrieval, and interactive document layout analysis; reviews several full working prototypes of multimodal interactive PR applications, including live demonstrations that can be publicly accessed on the Internet.

Computer-aided detection of prostate cancer

BACKGROUND Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment. MATERIAL AND METHODS A method for the analysis of transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper. First, two classifiers based on k-nearest neighbors and Hidden Markov models are compared. Second, the diagnostic capacity of our system is tested by means of a set of experiments where humans with varying degrees of experience classified a set of ultrasound images with and without the aid of the computer-aided system. The corpus used in this study was specifically acquired for this purpose. It consists of 4944 ultrasound images corresponding to 303 patients, and is publicly available for non-commercial use upon request. RESULTS The best classification results achieve an area under the receiver operating characteristic curve of 61.6%. However, the diagnostic capacity of an expert urologist using the computer-aided system improves only slightly compared with his/her capacity without the aid of the system. CONCLUSIONS Despite the difficulty of this task, the obtained results indicate that discrimination between cancerous and non-cancerous tissue is possible to a certain degree. The computer-aided system helps an inexperienced user to make a better diagnosis, however it must be able to perform better in order to be useful in a real-world clinical context.

The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition

Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies.

Computer Assisted Transcription of Handwritten Text Images

To date, automatic handwriting recognition systems are far from being perfect and often they need a post editing where a human intervention is required to check and correct the results of such systems. We propose to have a new interactive, on-line framework which, rather than full automation, aims at assisting the human in the proper recognition- transcription process; that is, facilitate and speed up their transcription task of handwritten texts. This framework combines the efficiency of automatic handwriting recognition systems with the accuracy of the human transcriptor. The best result is a cost-effective perfect transcription of the handwriting text images.

Projection Profile Based Algorithm for Slant Removal

The slant is one of the main sources of handwritten text variability. The slant is the clockwise angle between the vertical direction and the vertical text strokes. A well formalised and fast method to estimate the slant angle is presented. The method is based on the observation that the columns distribution of the vertical projection profile presents a maximum variance for the non slanted text. A comparative with Sobel operators convolution method and the Idiap slant method is provided.

Multimodal Interactive Handwritten Text Transcription

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Computer assisted transcription for ancient text images

Paleography experts spend many hours transcribing ancient documents and state-of-the-art handwritten text recognition systems are not suitable for performing this task automatically. We propose here a new interactive, on-line framework which, rather than full automation, aims at assisting the experts in the proper recognition-transcription process; that is, facilitate and speed up the transcription of old documents. This framework combines the efficiency of automatic handwriting recognition systems with the accuracy of the experts, leading to a cost-effective perfect transcription of ancient manuscripts.

Spontaneous handwriting recognition and classification

Finite-state models are used to implement a handwritten text recognition and classification system for a real application entailing casual, spontaneous writing with large vocabulary. Handwritten short paragraphs are to be classified into a small number of predefined classes. The paragraphs involve a wide variety of writing styles and contain many non-textual artifacts. HMMs and n-grams are used for text recognition and n-grams are also used for text classification. Experimental results are reported which, given the extreme difficulty of the task, are encouraging.

Computer Assisted Transcription of Text Images and Multimodal Interaction

Current automatic handwriting text image recognition systems are far from being perfect and, in general, human intervention is required to check and correct the results of such systems. This is both inefficient and uncomfortable to the user. As an alternative to this post-editing process, a multimodal interactive approach is proposed, where user feedback is provided by means of touch-screen pen strokes and/or more traditional keyboard and mouse operation. Experiments suggest that, using this approach, significant amounts of user effort can be saved with respect to the conventional, non-interactive, post-editing process.