Accuracy Affecting Factors for Optical Handwritten Character Recognition

Optiline kirjatuvastus viitab tehnikale, mis konverteerib trükitud, kirjutatud või prinditud teksi masinkodeeritud tekstiks, võimaldades sellega paberdokumentide nagu passide, arvete, meditsiiniliste vormide või tšekkide automaatset töötlemist. Mustrituvastus, tehisintellekt ja arvuti nägemine on kõik teadusharud, mis võimaldavad optilist kirjatuvastust. Optilise kirjatuvastuse kasutus võimaldaks paljudel kasvavatel informatsiooni süsteemidel mugavat üleminekut paberformaadilt digitaalsele. Tänapäeval on optilisest kirjatuvastusest väljaskasvanud mitme sammuline protsess: segmenteerimine, andmete eeltöötlus, iseloomulike tunnuste tuletamine, klassifitseerimine, andmete järeltöötlus ja rakenduse spetsiifiline optimiseerimine. See lõputöö pakub välja tehnikaid, millega üleüldiselt tõsta optiliste kirjatuvastussüsteemide täpsust, näidates eeltöötluse, iseloomulike tunnuste tuletamise ja morfoloogilise töötluse mõju. Lisaks võrreldakse erinevate enimkasutatud klassifitseerijate tulemusi. Kasutades selles töös mainitud meetodeid saavutati täpsus üle 98% ja koguti märkimisväärselt suur andmebaas käsitsi kirjutatud jaapani keele hiragana tähestiku tähti.Optical character recognition (OCR) refers to a technique that converts images of typed, handwritten or printed text into machine-encoded text enabling automatic processing paper records such as passports, invoices, medical forms, receipts, etc. Pattern recognition, artificial intelligence and computer vision are all research fields that enable OCR. Using OCR on handwritten text could greatly benefit many of the emerging information systems by ensuring smooth transition from paper format to digital world. Nowadays, OCR has evolved into a multi-step process: segmentation, pre-processing, feature extraction, classification, post-processing and application-specific optimization. This thesis proposes techniques to improve the overall accuracy of the OCR systems by showing the affects of pre-processing, feature extraction and morphological processing. It also compares accuracies of different well-known and commonly used classifiers in the field. Using the proposed techniques an accuracy of over 98% was achieved. Also a dataset of handwritten Japanese Hiragana characters with a considerable variability was collected as a part of this thesis

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Graph-based Object Understanding

Computer Vision algorithms become increasingly prevalent in our everyday lives. Especially recognition systems are often employed to automatize certain tasks (i.e. quality control). In State-of-the-Art approaches global shape char acteristics are leveraged, discarding nuanced shape varieties in the individual parts of the object. Thus, these systems fall short on both learning and utilizing the inherent underlying part structures of objects. By recognizing common substructures between known and queried objects, part-based systems may identify objects more robustly in lieu of occlusion or redundant parts. As we observe these traits, there are theories that such part-based approaches are indeed present in humans. Leveraging abstracted representations of decomposed objects may additionally offer better generalization on less training data. Enabling computer systems to reason about objects on the basis of their parts is the focus of this dissertation. Any part-based method first requires a segmentation approach to assign object regions to individual parts. Therefore, a 2D multi-view segmentation approach for 3D mesh segmentation is extended. The approach uses the normal and depth information of the objects to reliably extract part boundary contours. This method significantly reduces training time of the segmentation model compared to other segmentation approaches while still providing good segmentation results on the test data. To explore the benefits of part-based systems, a symbolic object classification dataset is created that inherently adheres to underlying rules made of spatial relations between part entities. This abstract data is also transformed into 3D point clouds. This enables us to benchmark conventional 3D point cloud classification models against the newly developed model that utilizes ground truth symbol segmentations for the classification task. With the new model, improved classification performance can be observed. This offers empirical evidence that part segmentation may boost classification accuracy if the data obey part-based rules. Additionally, prediction results of the model on segmented 3D data are compared against a modified variant of the model that directly uses the underlying symbols. The perception gap, representing issues with extracting the symbols from the segmented point clouds, is quantified. Furthermore, a framework for 3D object classification on real world objects is developed. The designed pipeline automatically segments an object into its parts, creates the according part graph and predicts the object class based on the similarity to graphs in the training dataset. The advantage of subgraph similarity is utilized in a second experiment, where out-of-distribution samples ofobjects are created, which contain redundant parts. Whereas traditional classification methods working on the global shape may misinterpret extracted feature vectors, the model creates robust predictions. Lastly, the task of object repairment is considered, in which a single part of the given object is compromised by a certain manipulation. As human-made objects follow an underlying part structure, a system to exploit this part structure in order to mend the object is developed. Given the global 3D point cloud of a compromised object, the object is automatically segmented, the shape features are extracted from the individual part clouds and are fed into a Graph Neural Network that predicts a manipulation action for each part. In conclusion, the opportunities of part-graph based methods for object understanding to improve 3D classification and regression tasks are explored. These approaches may enhance robotic computer vision pipelines in the future.2021-06-2

NON-LINEAR AND SPARSE REPRESENTATIONS FOR MULTI-MODAL RECOGNITION

In the first part of this dissertation, we address the problem of representing 2D and 3D shapes. In particular, we introduce a novel implicit shape representation based on Support Vector Machine (SVM) theory. Each shape is represented by an analytic decision function obtained by training an SVM, with a Radial Basis Function (RBF) kernel, so that the interior shape points are given higher values. This empowers support vector shape (SVS) with multifold advantages. First, the representation uses a sparse subset of feature points determined by the support vectors, which significantly improves the discriminative power against noise, fragmentation and other artifacts that often come with the data. Second, the use of the RBF kernel provides scale, rotation, and translation invariant features, and allows a shape to be represented accurately regardless of its complexity. Finally, the decision function can be used to select reliable feature points. These features are described using gradients computed from highly consistent decision functions instead of conventional edges. Our experiments on 2D and 3D shapes demonstrate promising results. The availability of inexpensive 3D sensors like Kinect necessitates the design of new representation for this type of data. We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. CORS produces compact and discriminative descriptors, which makes it more robust to noise and occlusions. It is also well-known to computer vision researchers that there is no universal representation that is optimal for all types of data or tasks. Sparsity has proved to be a good criterion for working with natural images. This motivates us to develop efficient sparse and non-linear learning techniques for automatically extracting useful information from visual data. Specifically, we present dictionary learning methods for sparse and redundant representations in a high-dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and KSVD can be made non-linear. We analyse their kernel constructions and demonstrate their effectiveness through several experiments on classification problems. It is shown that non-linear dictionary learning approaches can provide significantly better discrimination compared to their linear counterparts and kernel PCA, especially when the data is corrupted by different types of degradations. Visual descriptors are often high dimensional. This results in high computational complexity for sparse learning algorithms. Motivated by this observation, we introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lower-dimensional one in a way that preserves the sparse structure of data. We propose an efficient optimization algorithm and present its non-linear extension based on the kernel methods. One of the key features of our method is that it is computationally efficient as the learning is done in the lower-dimensional space and it discards the irrelevant part of the signal that derails the dictionary learning process. Various experiments show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive algorithms on signal recovery and object classification tasks. In many practical applications, we are often confronted with the situation where the data that we use to train our models are different from that presented during the testing. In the final part of this dissertation, we present a novel framework for domain adaptation using a sparse and hierarchical network (DASH-N), which makes use of the old data to improve the performance of a system operating on a new domain. Our network jointly learns a hierarchy of features together with transformations that rectify the mismatch between different domains. The building block of DASH-N is the latent sparse representation. It employs a dimensionality reduction step that can prevent the data dimension from increasing too fast as traversing deeper into the hierarchy. Experimental results show that our method consistently outperforms the current state-of-the-art by a significant margin. Moreover, we found that a multi-layer {DASH-N} has an edge over the single-layer DASH-N

Shape Representation in Primate Visual Area 4 and Inferotemporal Cortex

The representation of contour shape is an essential component of object recognition, but the cortical mechanisms underlying it are incompletely understood, leaving it a fundamental open question in neuroscience. Such an understanding would be useful theoretically as well as in developing computer vision and Brain-Computer Interface applications. We ask two fundamental questions: “How is contour shape represented in cortex and how can neural models and computer vision algorithms more closely approximate this?” We begin by analyzing the statistics of contour curvature variation and develop a measure of salience based upon the arc length over which it remains within a constrained range. We create a population of V4-like cells – responsive to a particular local contour conformation located at a specific position on an object’s boundary – and demonstrate high recognition accuracies classifying handwritten digits in the MNIST database and objects in the MPEG-7 Shape Silhouette database. We compare the performance of the cells to the “shape-context” representation (Belongie et al., 2002) and achieve roughly comparable recognition accuracies using a small test set. We analyze the relative contributions of various feature sensitivities to recognition accuracy and robustness to noise. Local curvature appears to be the most informative for shape recognition. We create a population of IT-like cells, which integrate specific information about the 2-D boundary shapes of multiple contour fragments, and evaluate its performance on a set of real images as a function of the V4 cell inputs. We determine the sub-population of cells that are most effective at identifying a particular category. We classify based upon cell population response and obtain very good results. We use the Morris-Lecar neuronal model to more realistically illustrate the previously explored shape representation pathway in V4 – IT. We demonstrate recognition using spatiotemporal patterns within a winnerless competition network with FitzHugh-Nagumo model neurons. Finally, we use the Izhikevich neuronal model to produce an enhanced response in IT, correlated with recognition, via gamma synchronization in V4. Our results support the hypothesis that the response properties of V4 and IT cells, as well as our computer models of them, function as robust shape descriptors in the object recognition process

Feature design and lexicon reduction for efficient offline handwriting recognition

This thesis establishes a pattern recognition framework for offline word recognition systems. It focuses on the image level features because they greatly influence the recognition performance. In particular, we consider two complementary aspects of prominent features impact: lexicon reduction and the actual recognition. The first aspect, lexicon reduction, consists in the design of a weak classifier which outputs a set of candidate word hypotheses given a word image. Its main purpose is to reduce the recognition computational time while maintaining (or even improving) the recognition rate. The second aspect is the actual recognition system itself. In fact, several features exist in the literature based on different fields of research, but no consensus exists concerning the most promising ones. The goal of the proposed framework is to improve our understanding of relevant features in order to build better recognition systems. For this purpose, we addressed two specific problems: 1) feature design for lexicon reduction (application to Arabic script), and 2) feature evaluation for cursive handwriting recognition (application to Latin and Arabic scripts). Few methods exist for lexicon reduction in Arabic script, unlike Latin script. Existing methods use salient features of Arabic words such as the number of subwords and diacritics, but totally ignore the shape of the subwords. Therefore, our first goal is to perform lexicon reductionn based on subwords shape. Our approach is based on shape indexing, where the shape of a query subword is compared to a labeled database of sample subwords. For efficient comparison with a low computational overhead, we proposed the weighted topological signature vector (W-TSV) framework, where the subword shape is modeled as a weighted directed acyclic graph (DAG) from which the W-TSV vector is extracted for efficient indexing. The main contributions of this work are to extend the existing TSV framework to weighted DAG and to propose a shape indexing approach for lexicon reduction. Good performance for lexicon reduction is achieved for Arabic subwords. Nevertheless, the performance remains modest for Arabic words. Considering the results of our first work on Arabic lexicon reduction, we propose to build a new index for better performance at the word level. The subword shape and the number of subwords and diacritics are all important components of Arabic word shape. We therefore propose the Arabic word descriptor (AWD) which integrates all the aforementioned components. It is built in two steps. First, a structural descriptor (SD) is computed for each connected component (CC) of the word image. It describes the CC shape using the bag-of-words model, where each visual word represents a different local shape structure. Then, the AWD is formed by concatenating the SDs using an efficient heuristic, implicitly discriminating between subwords and diacritics. In the context of lexicon reduction, the AWD is used to index a reference database. The main contribution of this work is the design of the AWD, which integrates lowlevel cues (subword shape structure) and symbolic information (subword counts and diacritics) into a single descriptor. The proposed method has a low computational overhead, it is simple to implement and it provides state-of-the-art performance for lexicon reduction on two Arabic databases, namely the Ibn Sina database of subwords and the IFN/ENIT database of words. The last part of this thesis focuses on features for word recognition. A large body of features exist in the literature, each of them being motivated by different fields, such as pattern recognition, computer vision or machine learning. Identifying the most promising approaches would improve the design of the next generation of features. Nevertheless, because they are based on different concepts, it is difficult to compare them on a theoretical ground and efficient empirical tools are needed. Therefore, the last objective of the thesis is to provide a method for feature evaluation that assesses the strength and complementarity of existing features. A combination scheme has been designed for this purpose, in which each feature is evaluated through a reference recognition system, based on recurrent neural networks. More precisely, each feature is represented by an agent, which is an instance of the recognition system trained with that feature. The decisions of all the agents are combined using a weighted vote. The weights are jointly optimized during a training phase in order to increase the weighted vote of the true word label. Therefore, they reflect the strength and complementarity of the agents and their features for the given task. Finally, they are converted into a numerical score assigned to each feature, which is easy to interpret under this combination model. To the best of our knowledge, this is the first feature evaluation method able to quantify the importance of each feature, instead of providing a ranking based on the recognition rate. Five state-of-the-art features have been tested, and our results provide interesting insight for future feature design

Motion-capture-based hand gesture recognition for computing and control

This dissertation focuses on the study and development of algorithms that enable the analysis and recognition of hand gestures in a motion capture environment. Central to this work is the study of unlabeled point sets in a more abstract sense. Evaluations of proposed methods focus on examining their generalization to users not encountered during system training. In an initial exploratory study, we compare various classification algorithms based upon multiple interpretations and feature transformations of point sets, including those based upon aggregate features (e.g. mean) and a pseudo-rasterization of the capture space. We find aggregate feature classifiers to be balanced across multiple users but relatively limited in maximum achievable accuracy. Certain classifiers based upon the pseudo-rasterization performed best among tested classification algorithms. We follow this study with targeted examinations of certain subproblems. For the first subproblem, we introduce the a fortiori expectation-maximization (AFEM) algorithm for computing the parameters of a distribution from which unlabeled, correlated point sets are presumed to be generated. Each unlabeled point is assumed to correspond to a target with independent probability of appearance but correlated positions. We propose replacing the expectation phase of the algorithm with a Kalman filter modified within a Bayesian framework to account for the unknown point labels which manifest as uncertain measurement matrices. We also propose a mechanism to reorder the measurements in order to improve parameter estimates. In addition, we use a state-of-the-art Markov chain Monte Carlo sampler to efficiently sample measurement matrices. In the process, we indirectly propose a constrained k-means clustering algorithm. Simulations verify the utility of AFEM against a traditional expectation-maximization algorithm in a variety of scenarios. In the second subproblem, we consider the application of positive definite kernels and the earth mover\u27s distance (END) to our work. Positive definite kernels are an important tool in machine learning that enable efficient solutions to otherwise difficult or intractable problems by implicitly linearizing the problem geometry. We develop a set-theoretic interpretation of ENID and propose earth mover\u27s intersection (EMI). a positive definite analog to ENID. We offer proof of EMD\u27s negative definiteness and provide necessary and sufficient conditions for ENID to be conditionally negative definite, including approximations that guarantee negative definiteness. In particular, we show that ENID is related to various min-like kernels. We also present a positive definite preserving transformation that can be applied to any kernel and can be used to derive positive definite EMD-based kernels, and we show that the Jaccard index is simply the result of this transformation applied to set intersection. Finally, we evaluate kernels based on EMI and the proposed transformation versus ENID in various computer vision tasks and show that END is generally inferior even with indefinite kernel techniques. Finally, we apply deep learning to our problem. We propose neural network architectures for hand posture and gesture recognition from unlabeled marker sets in a coordinate system local to the hand. As a means of ensuring data integrity, we also propose an extended Kalman filter for tracking the rigid pattern of markers on which the local coordinate system is based. We consider fixed- and variable-size architectures including convolutional and recurrent neural networks that accept unlabeled marker input. We also consider a data-driven approach to labeling markers with a neural network and a collection of Kalman filters. Experimental evaluations with posture and gesture datasets show promising results for the proposed architectures with unlabeled markers, which outperform the alternative data-driven labeling method

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches