Online writer identification system using adaptive sparse representation framework

IET BiometricsVolume 9, Issue 3 p. 126-133 Research ArticleFree Access Online writer identification system using adaptive sparse representation framework Vivek Venugopal, Vivek Venugopal Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039 IndiaSearch for more papers by this authorSuresh Sundaram, Corresponding Author Suresh Sundaram sureshsundaram@iitg.ac.in Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039 IndiaSearch for more papers by this author Vivek Venugopal, Vivek Venugopal Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039 IndiaSearch for more papers by this authorSuresh Sundaram, Corresponding Author Suresh Sundaram sureshsundaram@iitg.ac.in Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039 IndiaSearch for more papers by this author First published: 26 March 2020 https://doi.org/10.1049/iet-bmt.2019.0147Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding to the sub-strokes of a set of enrolled writers. The second component is a similarity score, computed for each dictionary atom with regards to a given writer, that is related to the reconstruction error of the sub-stroke based feature vectors. The proposed identification is accomplished with an ensemble of support vector machines (SVMs), wherein the input to the SVM trained for a writer is obtained by incorporating the adapted saliency values of that writer on the document descriptor obtained via average pooling of sparse codes. Experiments performed on the IAM and IBM-UB1 online handwriting databases demonstrate the efficacy of the proposed scheme. 1 Introduction Several researchers have been attracted to the idea of utilising handwriting of an individual as a behavioural biometric. The use of handheld devices such as tablets with an electronic pen/stylus has made it possible to capture the dynamic information present in the handwritten trace of a writer. Systems that make use of such textual content for identification are termed 'online'. In particular, the input is a set of strokes, each of which consists of a sequence of points. The research focus of this paper is on developing a text-independent online writer identification system, wherein the characteristics of a writer are learnt to obtain the authorship irrespective of the textual content of the document. One of the seminal works of online writer identification is that of the Gaussian mixture model-universal background model (GMM-UBM)-based system [1]. In the training phase, the first step involves the construction of a universal background model that characterise the feature vector distribution of an average writer. Subsequently, the GMMs specific to each writer is learnt through an adaptation process. The authorship of the document is assigned to the writer, whose GMM provides the best likelihood score in the testing phase. Researchers have also been inspired by techniques from the information retrieval domain to develop online writer identification systems [2]. This work adapts the idea of term frequency-inverse document frequency based formulation to score the distribution of allographs in the handwritten data. In another work [3], the authors employ the Latent Dirichlet Allocation (popular in the field of topic models) to characterise a writer's handwriting. An exploration to represent the feature vectors from a document with descriptors derived from a pre-generated codebook can be found in [4, 5]. Another direction of research is in modelling the trace of the handwritten strokes through shape primitives. An identification system based on the Bayesian methodology on the segmented shape primitives is proposed in [6]. The utility of employing a temporal sequence and shape codes to encode online handwriting has been studied in [7]. Likewise, the different strokes present in a document are represented in [8] by a set of histograms/probability distribution functions. Explorations were also made with regards to the generation of prototypes/writing styles. In work [9], the use of subtractive clustering is suggested to capture the various writing styles/prototypes of handwriting. The authors in [10, 11] employ the utility of Beta-elliptic model, to describe the spatial and velocity profiles of the segmented sub-stroke. In another work, the writing styles present in the pre-segmented graphemes are represented by using the multi-fractal concept [12]. Recently, deep learning-based approaches have captured the interest of the online writer identification research community. These methods learn the feature representation from the handwritten data, thus alleviating the need for hand-crafted features. A convolutional neural network-based approach was presented in [13], where the authors employ a drop segment method (influenced by the drop-out training strategy of [14]) to improve the generalisation capability. The authors in [15] explored a recurrent neural network model with bi-directional long short term memory-based approach for online writer identification. In this work, handwritten data of a given writer is represented by a set of random hybrid strokes (RHSs). Each RHS corresponds to a randomly sampled short sequence comprising the pen tip movements. These are then converted to a fixed-length vector for final classification by the deep-learning framework. A stacked sparse autoencoder network is proposed by the authors in [16] that operate on features derived from the Extended Beta-Elliptic model and the Fuzzy Elementary Perceptual Codes. In this paper, an adaptive sparse representation approach is employed for the task of online writer identification. Specifically, a strategy is proposed to modify the conventional average pooling based approach by considering prior knowledge derived from a set of dictionary atoms. The a-priori information quantifies the importance of each of the dictionary atoms with regards to the dynamic characteristics of a given writer. Accordingly, the term 'writer-specific saliency' [The term saliency is not to be interpreted as the one used by researchers in image processing community.] is defined for a dictionary atom - the value of which is derived by a fusion of two components. The first component is a saliency measure learnt from the sum-pooled sparse coefficients corresponding to the sub-strokes of the enrolled handwritten data. This measure signifies the importance of a dictionary atom with regards to an average writer in the database. The second component is a similarity score, computed for each dictionary atom with regards to a writer. The score, as such, is derived from the average reconstruction error on the feature vectors of sub-strokes that are segmented from his/her handwriting only. The idea of associating saliency to dictionary atoms was investigated for the first time in the exploration [17]. Contrast to that, the proposal discussed in this paper improves upon the same in several aspects as enumerated below: The saliency values being proposed in the present study denote the importance of the atoms of the dictionary specific to a given writer. This is different from [17], where the computation of these values is suggested from the perspective of modelling the average dynamic characteristics of the writers under consideration. As a re-emphasis, the proposed writer-specific saliency values are obtained by the fusion of two components – saliency measure concerning the average writer and the similarity scores obtained for each atom for a given writer. Moreover, while deriving the first component in Section 2.1, the information obtained from a single histogram is utilised to formulate a function on the sum-pooled sparse coefficients. This approach is computationally less intensive in contrast to [17], wherein the saliency values were obtained by computing entropy measures on several histograms. In particular, the number of histograms corresponds to the size of the over-complete dictionary. The identification framework is proposed via an ensemble of SVMs, where the descriptor to an SVM trained for a writer is based on the saliency values adapted using his/her handwritten data. The final decision on the authorship is proposed as the maximum of the prediction score obtained from the SVMs. On the whole, this paper demonstrates an improvement in writer identification performance at both paragraph and text-line levels on the IAM and IBM-UB1 databases. 2 Proposed methodology Fig. 1 presents a pipeline of the approach. To begin with, a set of pre-processing techniques are applied on the online handwritten trace of a test document, following which, the data is segmented into sub-strokes. The resultant sub-strokes are then represented using histogram features that capture the shape information. The sparse coding of the feature vectors is achieved by using the atoms of an over-complete dictionary, that are learnt during the training phase. As previously mentioned, the primary purpose of this paper is to derive a different saliency value for each dictionary atom within the context of a specific writer. Figure 1Open in figure viewerPowerPoint Overview of the proposed methodology The descriptor for a writer's document is generated with the help of the sparse coefficients corresponding to the dictionary atoms in conjunction with the knowledge of the saliency values specifically adapted to that writer that is determined a-priori during training. The resulting descriptor is subsequently passed as an input to an SVM of the ensemble for obtaining the prediction scores. In particular, the ensemble comprises as many SVMs as the number of writers. The authorship of the test document is finally assigned by considering the maximum of the prediction score from these SVMs. In the next two subsections, the details of the proposed two components for obtaining the writer specific saliency values are elaborated. However, at the same time in the context of the present work, the following may be borne in mind: The preprocessing steps being used are those of isolated point removal, same time stamp removal and stroke merge elaborated in [18]. The generation of the sub-strokes is based on the two strategies discussed in [17]. The feature vectors used for dictionary learning and subsequent sparse coding correspond to the histogram set described in [18] with the bin size B set to ten. The dictionary atoms (denoted by ) are obtained by employing the algorithm of [19] implemented in the SPAMS optimisation toolbox. 2.1 Proposed sum-pooling based saliency computation To begin with, it is assumed that a dictionary of M atoms has been learned from the training documents of the writers enrolled to the system. Let the total number of sub-strokes obtained from all the enrolled documents be . Assuming the number of writers to be W, the sub-strokes can be divided into W subsets in a way that each subset comprises the feature vectors of all sub-strokes segmented from the handwritten training samples of a single writer. The cardinality of , denoted by corresponds to the number of sub-strokes for the kth writer. The present strategy for saliency computation relies on the information obtained from a single histogram. In particular, the sparse coefficients associated with the dictionary atom across all the writers are employed to get this histogram. Let denote the histogram with the number of bins set at M – the dictionary size. The values of the votes in the bins are specified by . To begin with, these votes are initialised to zero. The process of accumulation of the ith bin is proposed as follows: For every feature vector of the sub-stroke from the kth writer (in the subset ), we consider the normalised sparse coefficient associated with the ith atom . The present vote for the ith bin of is then accumulated with the magnitude of this sparse coefficient. The votes are accumulated across the sub-strokes of each of the W writers to obtain the histogram . Put differently, the votes in each bin denote the aggregation of the magnitude of the normalised sparse coefficients from all the writers. Let denote the sparse coefficient associated with the ith dictionary atom, as obtained from the jth sub-stroke of the kth writer. After the generation of the histogram , the vote for the ith bin is given by (1) where each of the summation terms (2) correspond to the normalised value of the sparse coefficient that lies between [0, 1]. The saliency for the dictionary atom is formulated as (3) Without loss of generality, the number of sub-strokes across all the W writers are greater than the votes received in each bin . Owing to this, the M values of the saliency computed from (3) are greater than 1. In order to reduce the dynamic range of the values of the saliency values, the following factor NF is employed to map them in the range [0, 1]. (4) It may be stated that the relationship proposed above bears semblance to the equation of inverse document frequency (idf) from the field of document retrieval. In idf based scoring, the emphasis is on providing a degree of importance to a given the word based on its occurrence in a set of documents [20]. In the context of the present work, this idea is analogised by encoding the degree of importance (or saliency) of a dictionary atom based on the sparse coefficients, that are derived from the feature vectors of the sub-strokes of the enrolled writers. For brevity, the proposed sum pooled based saliency computation is summarised in Algorithm 1. In the next section, a discussion is provided as to how the saliency values are incorporated into the traditional sparse framework, leading to a modified writer descriptor. Algorithm 1.Proposed sum pooling-based saliency value computation • Input: Histogram based feature vectors from sub-strokes corresponding to enrolled training documents of W writers. Dictionary D with M atoms . • Output: Saliency values • Algorithm: Compute: – the sparse coding coefficient vector for each feature vector . Initialise a histogram of M bins with zero votes. for every feature vector in do Utilise the normalised sparse coefficients to accumulate the votes across the M bins of (refer (1)). end for Compute the saliency of the M dictionary atoms using (3). Normalise the values of the saliency in the range [0, 1] with the factor in (4). 2.2 Proposed writer specific saliency value adaptation In this section, the idea of utilising saliency values is extended in a way, such that they signify the importance of each of the dictionary atoms for a given writer. In particular, the focus is on adapting them to the writer in question by considering the dynamic characteristics of his/her handwriting only. Fig. 2 presents a high-level pictorial view of the approach, which comprises two steps. In the first step, the saliency values are computed by utilising the method discussed earlier in Section 2.1. At this point, it may be worth recalling that each such computed value represents the importance of a dictionary atom with regards to the average dynamic characteristics of the writers. Without loss of generality, the saliency values at this step are obtained without adapting them to any specific writer. Figure 2Open in figure viewerPowerPoint Pictorial overview of the approach depicting the adaptation of saliency values to a given writer The added distinction comes via the second step, wherein a similarity score is formulated for each atom, based on the reconstruction errors obtained from the training feature vectors of the writer under consideration. Accordingly, for a dictionary size of M atoms, a set of M similarity scores are obtained to capture the intra-writer information. These are then fused using a simple linear combination with the M saliency values (obtained in the first step), thereby resulting in the adaptation (in the form of weights) to a specific writer. We now provide the mathematical framework for adapting the saliency values of the dictionary atoms to each of the W writers. To begin with, for every feature vector of the jth sub-stroke from the kth writer in , similarity scores are computed, that relate to the reconstruction errors obtained separately from each of the M atoms of the dictionary (5) The obtained similarity scores for a dictionary atom are then averaged with regards to each writer separately and normalised as follows: (6) where (7) is the average similarity score of the kth writer corresponding to the atom . Here again, it may be noted that the normalised values in (6) sum to one. In this proposal, for each atom , the adapted saliency value with regards to the kth writer is determined by a linear combination of the with (8) Here denotes the set of M saliency values obtained without adaptation to a specific writer by employing the strategy discussed in Section 2.1 and controls the influence of the two terms. 3 Proposed writer descriptor Given a test document comprising the feature vectors of sub-strokes, the first step is to sparse encode each of them with the atoms of the dictionary . The average pooling strategy of writer description is defined by the vector z of size M, where the ith element is written as (9) The notation stands for the indicator variable denoted by (10) In this work, an ensemble of SVMs is employed to assign the authorship of an input test document. The number of SVMs corresponds to the writers enrolled in the system. Each classifier is trained by using a radial basis function (RBF) kernel to predict the probable score of a specific writer. After that, the maximum of the scores obtained is used to retrieve the identity of the writer. During the training process of a specific SVM in the ensemble, the descriptors obtained from the enrolled documents of the writer in question are treated as positive samples. The negative samples comprise the descriptors from the training documents of all the remaining writers. Though this framework bears semblance to the one-versus-all SVM, there is an important distinction with regards to the data being employed to train the individual classifiers. In the present approach, the RBF parameters of the kth SVM of the ensemble (namely C and ) are learned from descriptors, obtained by incorporating the M saliency adapted values for the kth writer. Mathematically, for an input test document, the M dimensional vector z is first generated by the traditional average pooling strategy. Prior to utilising the kth SVM of the ensemble, this vector is modified by incorporating the M saliency adapted values pre-computed for the kth writer. In other words (11) By considering the vector to represent the saliency values for the kth writer, the above equation can be rewritten via an element by element multiplication operation as (12) Here . Fig. 3 provides an overview of the classification framework used in this paper. Moreover, for ease of clarity, a high-level summary of the proposed writer identification framework is presented in Algorithm 2 (see Fig. 4). Figure 3Open in figure viewerPowerPoint Block diagram of the proposed identification framework that makes use of an ensemble of SVMs to predict the author of the test document. The input to each of the SVM trained for a specific writer is a descriptor obtained by incorporating the appropriate saliency adapted values. For more details, refer the text Figure 4Open in figure viewerPowerPoint Algorithm 2: Proposed identification system based on writer adapted saliency values 3.1 Computational complexity Table 1 compares the computational complexity for the different steps of the proposed algorithm with the work in [17]. As mentioned earlier, the total number of feature vectors used for enrolment is denoted by , while W and M denote the number of writers and dictionary atoms in the over-complete dictionary, respectively. The notation corresponds to the number of training feature vectors belonging to the ith writer, whereas represents the number of feature vectors in the test document. From the entries, the following points may be observed: The histogram generation step (for the saliency computation of an average writer) using the approach in [17] has a computational complexity . In contrast to that, in the proposed methodology, it is . Added to the above, in the previous work, an additional computation of is required for the calculation of the entropy measure, which in turn are employed to generate the saliency values. Table 1. Comparison of the computational complexity of the writer-specific saliency computation and writer description with the approach described in [17] Description Method described in [17] Proposed methodology histogram generation entropy calculation n/a saliency computation of average writer Writer adaptation computation of scores in (5) n/a computation of scores in (6) n/a determination of writer adapted saliency values in (8) n/a Testing calculation of average pooled writer descriptor modified writer descriptor generation Here stands for not applicable', indicating that the corresponding operation is not relevant. For adapting the saliency values to the individual writers, we rely on the computation of similarity scores via (5) and (6). These two steps have a complexity of and , respectively. Following this, the linear combination (described in (8)) for computing the adapted saliency values require a complexity of . During the testing phase, for the average pooled writer description, both the algorithms possess a complexity of . However, it may be noted the generation of the modified descriptor incurs , while for the approach in [17], it is . The increase in computation can be attributed to the framework of an ensemble of SVMs, wherein each classifier is trained by employing descriptors obtained from the adapted saliency values of a particular writer. 4 Experimental set-up The experiments are conducted on the publicly available IAM and the IBM-UB1 databases. The former comprises handwritten documents of 217 writers, with each of them providing 8 paragraphs with 50 words [21]. The online trace contains the information of at each point. The IBM-UB1 database, on the other hand, consists of online handwritten data obtained on the IBM Cross-pad from 43 writers [22]. Each writer randomly selects a topic and writes two different documents, namely a summary and query. The textual content in the summary document is one to page essay on the topic selected, whereas the query consists of 25 keywords for the content in the summary document. The handwritten documents of this database have a recording of the spatial coordinates . For the IAM database, four paragraphs from each writer at random are considered for enrolment, with the remaining chosen for performance evaluation. As far as the IBM UB 1 database is concerned, the protocol described in [3] is used where in 80% of the summary document paragraphs belonging to a writer are chosen randomly for training and the remaining for testing. The dictionary learning is done by using the enrolled sub-strokes from 25% of the number of users randomly chosen. The performance of the proposed strategy is reported at both paragraph and text-line level by specifying the average writer identification rate obtained from a set of ten trials. A trial comprises a selection of documents for enrolment, dictionary learning, saliency computation for each writer, construction of descriptor and classification. 5 Performance evaluation For the present discussion, the various writer identification systems being implemented are first outlined, together with their abbreviations. SPF: This system corresponds to the traditional sparse representation framework based on average pooling strategy (without the incorporation of saliency). SP-SL: This system corresponds to the modified sparse representation framework based on average pooling with the incorporation of saliency obtained from the sum-pooling approach (without adaptation). SAL-ADP: refers to the system where the modified writer descriptor is computed by incorporating writer-specific saliency values to the writer descriptor obtained via the traditional average pooling strategy. 5.1 Effect of sum-pooling based saliency values In this experiment, the performance of the SP-SL for writer identification is compared with the traditional SPF system. Tables 2(a) and (b) depict the results for varying dictionary sizes on the IAM and IBM-UB1 databases, respectively. Table 2. Comparison of average identification rates (in %) of the SPF and SP-SL systems for different dictionary sizes M Dictionary size, M Paragraph level Text level SPF system SP-SL system SPF system SP-SL system (a) IAM database 50 92.45 92.92 64.29 66.40 100 93.33 94.15 71.19 72.07 150 94.50 95.83 74.42 75.73 200 95.84 96.62 78.70 79.11 250 96.78 97.48 80.99 82.03 300 97.65 98.11 84.99 85.97 350 98.09 98.57 86.51 87.49 400 98.41 99.07 87.69 88.61 450 98.27 98.96 87.44 88.35 500 98.16 98.89 87.25 88.16 (b) IBM-UB1 database 50 72.65 73.34 52.53 53.05 100 76.16 77.10 60.25 61.17 150 80.29 81.16 64.95 66.75 200 83.14 83.89 69.35 70.13 250 87.28 88.07 72.14 73.07 300 91.10 91.92 76.30 77.35 350 93.64 94.03 78.25 79.11 400 95.16 95.78 79.47 80.35 450 95.03 95.64 79.29 80.25 500 94.91 95.53 79.18 80.18 From Table 2(a), the SP-SL system gives the best average identification rate of 99.07% for at the paragraph level on the IAM database. In contrast, the traditional descriptors of the SPF system trails, with a performance of 98.41% again at . With regards to the text-line level, an improvement from 87.69% (SPF system) to 88.61% (SP-SL system) can be noted. On the whole, for both paragraph and text-line levels at different dictionary sizes, the modified writer descriptor with saliency provides a higher identification rate than that obtained without their incorporation. A similar trend of improved results can be observed in Table 2(b) for the IBM-UB1 database. 5.2 Effect of writer-specific adaptation of saliency values In this section, the efficacy of the SAL-ADP system is demonstrated, wherein the saliency values are adapted for each writer separately by considering the linear combination in (8). To begin with, the influence of the weight on the identification performance is investigated by varying its value from zero to one in steps of 0.1. The sum-pooling based approach discussed in Section 2.1 is employed for obtaining the saliency of dictionary atoms. Moreover, the descriptor to each SVM in the ensemble is obtained by incorporating the adapted saliency values to the SPF system, that is based on the average pooling strategy. For this experiment, the size of the dictionary is 400. The best average identification rates are presented in Table 3 for both the databases. As for the trend, the identification rate increases with , attains a maximum and then decreases. The best identification rates at paragraph and text line levels are obtained at a value of 0.5 and 0.6 for the IAM and the IBM-UB1 databases, respectively. It may be noted here that the value relates to the performance of the SP-SL system. Table 3. Influence of the weighting factor used for adapting the saliency values on writer identification for the SAL-ADP system Database Training framework value 0 0.2 0.4 0.5 0.6 0.8 1 IAM paragraph 99.07 99.26 99.43 99.69 99.31 99.02 98.77 text line 88.61 89.42 90.37 91.48 90.21 89.15 88.04 IBM-UB1 paragraph 95.78 96.19 96.57 97.08 97.44 96.98 96.77 text line 80.35 81.20 82.09 83.04 83.87 83.11 82.49 Further to the above, the performance of the SAL-ADP system is analysed for a different number of dictionary atoms M from 100 to 500 in increments of 50. The value of is also varied from 0 to 1 in steps of 0.1. From Table 4, it may be noted that the highest writer identification rates at paragraph and text line level for the IAM database are at 99.69 and 91.48%, respectively, for . Concerning the IBM-UB1 database, an identification rate of 97.44 and 83.87% is achieved – once again for the same dictionary size. Table 4. Influence of dictionary size on the modified writer descriptor obtained from the writer-specific saliency values in the SAL-ADP system Dictionary size, M IAM database IBM-UB1 database Paragraph level Text line level Paragraph level Text line level 100 97.66 0.4 75.21 0.5 79.14 0.6 63.08 0.5 200 98.71 0.6 82.16 0.4 84.85 0.5 72.13 0.6 250 99.03 0.6 84.90 0.6 88.97 0.6 75.65 0.5 300 99.27 0.5 87.63 0.5 92.82 0.5 79.50 0.4 350 99.50 0.4 89.29 0.4 94.75 0.4 81.44 0.5 400 99.69 0.5 91.48 0.5 97.44 0.6 83.87 0.6 450 99.43 0.4 91.22 0.4 97.29 0.6 83.51 0.4 500 99.25 0.5 90.97 0.6 97.06 0.5 83.16 0.4 5.3 Statistical significance For completeness, the p values as obtained by the Mc-Namer significance test, are presented in Table 5. The low values () in sub-table (a) do suggest that the results of saliency-based SAL-ADP and SP-SL are statistically significant with those of the traditional SPF system. A similar conclusion may be inferred by considering SAL-ADP with the proposal described in [17] (refer to sub-table (b)). Table 5. Statistical significance (a) Statistical significance of the SP-SL and SAL-ADP systems over the conventional SPF system Database Classification framework Paragraph level Text line level IAM SP-SL SAL-ADP IBM-UB1 SP-SL SAL-ADP (b) Statistical significance of the SAL-ADP systems with the framework in [17] Database Paragraph Level Text line level IAM IBM-UB1 5.4 Discussion to prior works In this subsection, we see how the performance of the proposed writer identification system fares to the previous works that use the IAM and IBM-UB1 databases (refer Table 6). It may be emphasised that a direct one-to-one comparison is not possible owing to different experimental set-ups, features and enrolment protocols. A competent performance to the state-of-the-art algorithms is observed at both paragraph and text line level. Table 6. Enumeration of prior online writer identification systems on the two databases Database Method Paragraph Text line IAM GMM-UBM [1] 98.56 88.96 Latent Dirichlet allocation [3] 93.39 — Subtractive clustering [9] 96.30 — k-means + Codebook descriptor [4] 97.81 80.61 Modified Codebook descriptor [5] 98.82 89.62 Sparse coding + histogram descriptors [23] 98.94 83.30 Sparse coded descriptors [18] 99.45 90.28 Sparse coding + saliency [17] 99.28 89.71 Proposed methodology 99.69 91.48 IBM-UB1 Latent Dirichlet Allocation [3] 89.47 — k-means + Codebook descriptor [4] 94.37 76.86 Modified Codebook descriptor [5] 96.10 81.59 Sparse coded descriptors [18] 97.21 83.49 Sparse coding + saliency [17] 96.37 81.61 Stacked sparse auto-encoders [16] 96.90 — Proposed methodology 97.44 83.87 The numbers being mentioned are the respective writer identification rates (in %). Entries marked with (—) symbol indicate that the respective authors did not report the results for their proposal. An important observation at this juncture is the improvement of the system over [17]. This can be attributed to the adaptation of saliency values of the dictionary atoms to each of the enrolled writers of the system. 6 Conclusion In this work, a sparse classification based writer identification system for online handwritten documents is proposed by introducing a-priori (saliency) information for the atoms of the dictionary. As an improvement to [17], the saliency values are computed with regards to each of the enrolled writers. In addition, an ensemble of SVMs is formulated, wherein the input to the SVM trained for a writer is based on the adapted saliency values for that writer. Promising results are reported on the handwritten samples of the IAM and IBM-UB1 databases, respectively. 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