We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained “on the fly ” with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram (LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models (SC-HMM), in which the typed-text models are constrained to a “vocabulary ” of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SC-HMM is crucial. To the best of the authors ’ knowledge, this is the first work to address this issue in a non-trivial manner. 1.

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Handwritten text is generally captured through two main modalities: off-line and on-line. Smart approaches to handwritten text recognition (HTR) may take advantage of both modalities if they are available. This is for instance the case in computer-assisted transcription of text images, where on-line text can be used to interactively correct errors made by a main off-line HTR system. We present here baseline results on the biMod-IAM-PRHLT corpus, which was recently compiled for experimentation with techniques aimed at solving the proposed multi-modal HTR problem, and is being used in one of the official ICPR-2010 contests. 1

Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on-line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of text images, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to establish baseline performance figures, we have also obtained uni-modal results for each modality, as well as bi-modal results using Handwritten text is one of the most natural communication channels currently available

To date, automatic handwriting recognition systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. This “post-editing ” process is both inefficient and uncomfortable to the user. An example is the transcription of historic documents: State-of-the-art handwritten text recognition technology is not suitable to perform this task automatically and expensive paleography expert work is needed to achive correct transcriptions. As an alternative to post-editing, a multimodal interactive approach is proposed here, where user feedback is provided by means of touch-screen pen strokes and/or more traditional keyboard and mouse operation. User’s feedback directly allows to improve system accuracy, while multimodality increases system ergonomy and user acceptability. Multimodal interaction is approached in such a way that both the main and the feedback data streams help each-other to optimize overall performance and usability. Empirical tests on three cursive handwritten tasks suggest that, using this approach, significant amounts of user Lately, the paradigm for Pattern Recognition (PR) systems design has been shifting

out-of-vocabulary words and recognition errors based on word

Building recognition systems for historical documents is a difficult task. Especially, when it comes to medieval scripts. The complexity is mainly affected by the poor quality and the small quantity of the data available. In this paper we apply an HMM based recognition system to medieval manuscripts from the 13th century written in Middle High German. The recognition system, which was originally developed for modern scripts, has been adapted to medieval scripts. Beside the data processing, one of the major challenges is to create a suitable language model. Because of the lack of appropriate independent text corpora for medieval languages, the language model has to be created on the base of a rather small number of manuscripts only. Due to the small size of the corpus, optimizing the language model parameters can quickly lead to the problem of overfitting. In this paper we describe a strategy to integrate all available information into the language model and to optimize the language model parameters without suffering from this problem. 1.

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black-box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a direct “phrase-based ” system in contrast to traditional sourcechannel models. We report the accuracies of an in-house handwritten word recognizer before and after the correction. We achieve highly encouraging results for a large dataset. 1

I thank the Almighty for providing me with this opportunity to serve Him and make a contribution through His infinite wisdom. I thank my parents for their perseverance and unconditional support, without which I could never have accomplished this endeavor. I would also like to thank other members of my family including my cousin Muneer who has been watching my back from day one. I want to extend my deep appreciation to Dr. Venu Govindaraju, the chair of my dissertation committee. He has been an advisor and a mentor. His persistent guidance, omnipresent motivation and overall support have been the foundation of this thesis. He introduced me to the area of handwriting recognition and encouraged me to address the open challenge of retrieval from handwritten documents. I want to show my gratitude to Dr. Peter Scott, member of my dissertation committee. His course Computer Vision and Image Processing indeed laid a solid foundation for iv this research. His guidance and advise has been always helpful. In addition, I had the opportunity to be his Teaching Assistant for three semesters and his passion for teaching was a great motivation.

Generally, handwritten word recognition systems use script specific methodologies. In this paper, we present a unified approach for multi-lingual recognition of alphabetic scripts. The proposed system operates independently of the nature of the script using the multi-stream paradigm. The experiments have been carried out on a multi-script database composed of Arabic and Latin handwritten words from the IFN/ENIT and the IRONOFF public databases and show interesting recognition performances with only 1.5 % of script confusion and an overall word recognition rate of 84.5 % using a multi-script lexicon of 1142 words. 1.