Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has ...led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.
Non-BET bromodomain-containing proteins have become attractive targets for the development of novel therapeutics targeting epigenetic pathways. To help facilitate the target validation of this class ...of proteins, structurally diverse small-molecule ligands and methodologies to produce selective inhibitors in a predictable fashion are in high demand. Herein, we report the development and application of atypical acetyl-lysine (KAc) methyl mimetics to take advantage of the differential stability of conserved water molecules in the bromodomain binding site. Discovery of the
-butyl group as an atypical KAc methyl mimetic allowed generation of
(GSK6776) as a soluble, permeable, and selective BRD7/9 inhibitor from a pyridazinone template. The
-butyl group was then used to enhance the bromodomain selectivity of an existing BRD9 inhibitor and to transform pan-bromodomain inhibitors into BRD7/9 selective compounds. Finally, a solvent-exposed vector was defined from the pyridazinone template to enable bifunctional molecule synthesis, and affinity enrichment chemoproteomic experiments were used to confirm several of the endogenous protein partners of BRD7 and BRD9, which form part of the chromatin remodeling PBAF and BAF complexes, respectively.
The low affinity metabotropic glutamate receptor mGluR7 has been implicated in numerous CNS disorders; however, a paucity of potent and selective activators has hampered full delineation of the ...functional role and therapeutic potential of this receptor. In this work, we present the identification, optimization, and characterization of highly potent, novel mGluR7 agonists. Of particular interest is the chromane CVN636, a potent (EC50 7 nM) allosteric agonist which demonstrates exquisite selectivity for mGluR7 compared to not only other mGluRs, but also a broad range of targets. CVN636 demonstrated CNS penetrance and efficacy in an in vivo rodent model of alcohol use disorder. CVN636 thus has potential to progress as a drug candidate in CNS disorders involving mGluR7 and glutamatergic dysfunction.
The low affinity metabotropic glutamate receptor mGluR
has been implicated in numerous CNS disorders; however, a paucity of potent and selective activators has hampered full delineation of the ...functional role and therapeutic potential of this receptor. In this work, we present the identification, optimization, and characterization of highly potent, novel mGluR
agonists. Of particular interest is the chromane
, a potent (EC
7 nM) allosteric agonist which demonstrates exquisite selectivity for mGluR
compared to not only other mGluRs, but also a broad range of targets.
demonstrated CNS penetrance and efficacy in an
rodent model of alcohol use disorder.
thus has potential to progress as a drug candidate in CNS disorders involving mGluR
and glutamatergic dysfunction.
Bromodomain containing proteins and the acetyl-lysine binding bromodomains contained therein are increasingly attractive targets for the development of novel epigenetic therapeutics. To help validate ...this target class and unravel the complex associated biology, there has been a concerted effort to develop selective small molecule bromodomain inhibitors. Herein we describe the structure-based efforts and multiple challenges encountered in optimizing a naphthyridone template into selective TAF1(2) bromodomain inhibitors which, while unsuitable as chemical probes themselves, show promise for the future development of small molecules to interrogate TAF1(2) biology. Key to this work was the introduction and modulation of the basicity of a pendant amine which had a substantial impact on not only bromodomain selectivity but also cellular target engagement.
In this paper we present IAM-OnDB - a new large online handwritten sentences database. It is publicly available and consists of text acquired via an electronic interface from a whiteboard. The ...database contains about 86 K word instances from an 11 K dictionary written by more than 200 writers. We also describe a recognizer for unconstrained English text that was trained and tested using this database. This recognizer is based on hidden Markov models (HMMs). In our experiments we show that by using larger training sets we can significantly increase the word recognition rate. This recognizer may serve as a benchmark reference for future research.
This paper is an effort towards the development of a shared conceptualization regarding automatic signature verification systems. The requirements of both communities, Pattern Recognition and ...Forensic Handwriting Examiners, are explicitly focused. This is required because an increasing gap regarding evaluation of automatic verification systems is observed in the recent past. The paper addresses three major areas. First, it highlights how signature verification is taken differently in the above mentioned communities and why this gap is increasing. Various factors that widen this gap are discussed with reference to some of the recent signature verification studies and probable solutions are suggested. Second, it discusses the state-of-the-art evaluation and its problems as seen by FHEs. The real evaluation issues faced by FHEs, when trying to incorporate automatic signature verification systems in their routine casework, are presented. Third, it reports a standardized evaluation scheme capable of fulfilling the requirements of both PR researchers and FHEs.
In this paper we propose a novel approach for online mode detection, where the task is to classify ink traces into several categories. In contrast to previous approaches working on global features, ...we introduce a system completely relying on local features. For classification, standard recurrent neural networks (RNNs) and the recently introduced long short-term memory (LSTM) networks are used. Experiments are performed on the publicly available IAMonDo-database which serves as a benchmark data set for several researches. In the experiments we investigate several RNN structures and classification sub-tasks of different complexities. The final recognition rate on the complete test set is 98.47% in average, which is significantly higher than the 97% achieved with an MCS in previous work. Further interesting results on different subsets are also reported in this paper.
Signature Segmentation from Document Images Ahmed, S.; Malik, M. I.; Liwicki, M. ...
2012 International Conference on Frontiers in Handwriting Recognition,
2012-Sept.
Conference Proceeding
In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In ...particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from signatures. Using SURF features makes the approach generally more useful and reliable for different resolution documents. We have evaluated our system on the publicly available Tobacco-800 dataset in order to compare it to previous work. Finally, all signatures were found in the images and less than half of the found signatures are false positives. Therefore, our system can be applied for practical use.
In this paper, orthogonal polynomials series are used to approximate the time functions associated to the signatures. The coefficients in these series expansions, computed resorting to least squares ...estimation techniques, are then used as features to model the signatures. Different combinations of several time functions (pen coordinates, incremental variation of pen coordinates and pen pressure), related to the signing process, are analyzed in this paper for two different signature styles, namely, Western signatures and Chinese signatures of a publicly available Signature Database. Two state-of-the-art classification methods, namely, Support Vector Machines and Random Forests are used in the verification experiments. The proposed online signature verification system delivers error rates comparable to results reported over the same signature datasets in a previous signature verification competition.
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