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Tran Scriptorium is a 3-years project that aims to develop innovative, cost-effective solutions for the indexing, search and full transcription of historical handwritten document images, using Handwritten Text Recognition (HTR) technology. The production of ground-truth (GT) of a dataset of handwritten document images is among the first tasks. We address novel approaches for the faster production...
Key-Word Spotting (KWS) in handwritten documents is approached here by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. Linguistic context significantly boost KWS performance with respect to methods which ignore word contexts and/or rely on image-matching with pre-segmented isolated words. On...
We propose a state-of-the-art system for recognizing real-world handwritten images exposing a huge degree of noise and a high out-of-vocabulary rate. We describe methods for successful image demising, line removal, deskewing, deslanting, and text line segmentation. We demonstrate how to use a HMM-based recognition system to obtain competitive results, and how to further improve it using LSTM neural...
The segmentation of magnetic resonance data is a challenging task, essential to several clinical and research applications. Since they do not require assistance from a human expert, unsupervised segmentation approaches are especially useful for this task. In this paper, we present two novel unsuper-vised segmentation methods based on random walks. The proposed methods find the probability mode in...
Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded over 100 videos,...
Real-time moving object detection, classification and tracking capabilities are presented with its system operate on both color and gray scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. The tracking of moving objects in a video sequence is an important task in different domains such as video...
In this paper, we propose an analytical approach of an offline recognition of handwritten Arabic. Our method is based on Hidden Markov Models (HMM) Toolkit (HTK), modeling type that takes into consideration the characteristics of Arabic script and possible inclinations of cursive words. The objective is to propose a methodology for rapid implementation of our approach. To this end, a preprocessing...
Based on a new variational-based model within the fuzzy framework, we propose a new solution to the problem of multi-region segmentation of natural images. The advantages of our model is: by introducing the PCA features and modeling regions by Gaussian distribution, the proposed model can partition texture images better than classical variational-based segmentation models. We use the Berkeley database(BSDS300)...
New approaches of eye state detection and eye sequence identification for computer interface of paralyzed patients are proposed. In this work, patients can interact via sequences of four eye states that are close, forward-glance, rightward-glance, and leftward-glance states. To detect the eye states, eye images are firstly segmented by using FCM clustering scheme in a feature space of RGB color components...
Recognition of curved text in natural scene image is a challenging task. Due to complex background and unpredictable characteristics of scene text and noise, text characters in strings are often touching that affects the performance of segmentation and recognition. This paper presents a novel approach for curved text recognition using Hidden Markov Models (HMM). From curved text, a path of sliding...
This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities. In fact, this work is based on a dynamic hierarchical Bayesian network. The aim is to find the best model of Arabic handwriting to reduce the complexity of the recognition process by permitting the partial recognition. Actually, we propose a segmentation of the word...
This work deals with the use of a probabilistic quad-tree graph (Hidden Markov Tree, HMT) to provide fast computation, improved robustness and an effective interpretational framework for image analysis and processing in oncology. Thanks to two efficient aspects (multi observation and multi resolution) of HMT and Bayesian inference, we exploited joint statistical dependencies between hidden states...
The text appears in the images is important for fully understanding the images. The number of digital images and digital videos has increased tremendously. Although there are many methods have been proposed over the past years for the text extraction from natural scene images, the text detection and extraction from born-digital images are still a challenge. In this paper, we describe existing methods...
LSTM-RNN has been succeeded in applying in offline handwritten recognition. The paper used two-dimensional LSTM-RNN to recognize text-based CAPTCHA. Aiming at the problem that traditional decoding algorithm cannot obtain satisfactory results. The paper proposed a novel decoding algorithm based on the multi-population genetic algorithm. The experimental results showed that the novel decoding algorithm...
Image segmentation is one of the fundamental problems in computer vision. In this work, we present a new segmentation algorithm that is based on the theory of two-dimensional hidden Markov models (2D-HMM). Unlike most 2D-HMM approaches we do not apply the Viterbi Algorithm, instead we present a computationally efficient algorithm that propagates the state probabilities through the image. This approach...
Recently, an information-based saliency technique which is biologically plausible and computationally feasible called Discriminant Saliency (DIS) has been proposed. While DIS successfully defines discriminant saliency in the information theoretic sense, its implementation restraints the sampled features to a single fixed-size window and creates a bias towards objects with distinctive features fitted...
Locating an accurate desired object boundary using active contours and deformable models plays an important role in computer vision, particularly in medical imaging applications. Powerful segmentation methods have been introduced to address limitations associated with initialization and poor convergence to boundary concavities. This paper proposes a method to improve one of the strongest and recent...
In contrast to the mainstream HMM-based approaches dedicated for the recognition of offline handwritten Arabic, this paper proposes an HMM-based approach that built upon an explicit segmentation module. And shape representative based rather than sliding window based features, are extracted and used to build a reference as well as a confirmation model for each letter in each handwritten form. Additionally,...
Optical Character Recognition (OCR) problems are often formulated as isolated character (symbol) classification task followed by a post-classification stage (which contains modules like Unicode generation, error correction etc.) to generate the textual representation, for most of the Indian scripts. Such approaches are prone to failures due to (i) difficulties in designing reliable word-to-symbol...
Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant...
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