Showing papers on "Intelligent word recognition published in 2013"

Recognizing Text with Perspective Distortion in Natural Scenes

Abstract: This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-key points approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations.

Scene Text Recognition Using Part-Based Tree-Structured Character Detection

Abstract: Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific structure information, we use part-based tree-structure to model each type of character so as to detect and recognize the characters at the same time. While for word recognition, we build a Conditional Random Field model on the potential character locations to incorporate the detection scores, spatial constraints and linguistic knowledge into one framework. The final word recognition result is obtained by minimizing the cost function defined on the random field. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods significantly both for character detection and word recognition.

A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits

Abstract: We propose a novel handwritten character recognition method for isolated handwritten Bangla digits. A feature is introduced for such patterns, the contour angular technique. It is compared to other methods, such as the hotspot feature, the gray-level normalized character image and a basic low-resolution pixel-based method. One of the goals of this study is to explore performance differences between dedicated feature methods and the pixel-based methods. The four methods are compared with support vector machine (SVM) classifiers on the collection of handwritten Bangla digit images. The results show that the fast contour angular technique outperforms the other techniques when not very many training examples are used. The fast contour angular technique captures aspects of curvature of the handwritten image and results in much faster character classification than the gray pixel-based method. Still, this feature obtains a similar recognition compared to the gray pixel-based method when a large training set is used. In order to investigate further whether the different feature methods represent complementary aspects of shape, the effect of majority voting is explored. The results indicate that the majority voting method achieves the best recognition performance on this dataset.

Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Abstract: This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

HACDB: Handwritten Arabic characters database for automatic character recognition

Abstract: Automatic off-line Arabic handwriting recognition based on segmentation still faces big challenges. A database, covering all shapes of handwritten Arabic characters, is required to facilitate the recognition process. This paper introduces a new database for handwritten Arabic characters (HACDB), designed to cover all shapes of Arabic characters including overlapping ones. It contains 6,600 shapes of characters written by 50 writers. This database can be used for training and testing the words for their recognition after segmentation. Also, it presents the possibility for comparing different approaches and evaluate their accuracy on a common base.

ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)

Abstract: This paper presents the results of the HDRC 2013 competition for recognition of handwritten digits organized in conjunction with ICDAR 2013. The general objective of this competition is to identify, evaluate and compare recent developments in character recognition and to introduce a new challenging dataset for benchmarking. We describe competition details including dataset and evaluation measures used, and give a comparative performance analysis of the nine (9) submitted methods along with a short description of the respective methodologies.

ICDAR 2013 CROHME: Third International Competition on Recognition of Online Handwritten Mathematical Expressions

Abstract: We report on the third international Competition on Handwritten Mathematical Expression Recognition (CROHME), in which eight teams from academia and industry took part. For the third CROHME, the training dataset was expanded to over 8000 expressions, and new tools were developed for evaluating performance at the level of strokes as well as expressions and symbols. As an informal measure of progress, the performance of the participating systems on the CROHME 2012 data set is also reported. Data and tools used for the competition will be made publicly available.

Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network

Abstract: Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-Learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme Learning Machine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.

Online Handwriting Recognition Using Levenshtein Distance Metric

Abstract: In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the trajectory of the handwritten character. The consecutive occurrences of same value in such a string are removed retaining only one of them. Next, each element in the resulting string is assigned an integral weight value proportional to the length of the segment of the trajectory represented by the corresponding element. Similarly, position information is encoded by another string of quantized positional information along with their respective weight values. We formulated a distance function based on Levenshtein metric to compute the similarity between an unknown character sample and each training sample. Here, we have also studied the effect of pruning the training sample set based on the above distance between individual training samples of the same character class. The proposed approach has been simulated on different publicly available sample databases of online handwritten characters. The recognition accuracies are acceptable.

Handwritten text segmentation using average longest path algorithm

Abstract: Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.

Deformable HOG-Based Shape Descriptor

Abstract: In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval.

Number plate recognition using ocr technique

Abstract: Automatic Number Plate Recognition (ANPR) is a spec ial form of Optical Character Recognition (OCR). AN PR is an image processing technology which identifies the vehicle from its number plate automatically by digital pict ures. In this paper we have presented an algorithm for vehicle number identific ation based on Optical Character Recognition (OCR). OCR is used to recognize an optically processed printed character number pla te which is based on template matching. This algori thm is tested on different ambient illumination vehicle images. OCR is the las t stage in vehicle number plate recognition . In recognition stage the characters on the number plate are converted into texts. The char acters are then recognized using the template match ing algorithm. Index Terms : Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Template Matching

Zernike moment feature extraction for handwritten Devanagari compound character recognition

Abstract: Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental tool to generate feature descriptors where the Zernike moment technique has a rotation invariance property which found to be desirable for handwritten character recognition. This paper discusses extraction of features from handwritten compound characters using Zernike moment feature descriptor and proposes SVM and k-NN based classification system. The proposed classification system preprocess and normalize the 27000 handwritten character images into 30×30 pixels images and divides them into zones. The pre-classification produces three classes depending on presence or absence of vertical bar. Further Zernike moment feature extraction is performed on each zone. The overall recognition rate of proposed system using SVM and k-NN classifier is upto 98.37%, and 95.82% respectively.

Feature extraction techniques of online handwriting arabic text recognition

Abstract: Online recognition of Arabic handwritten text has been an ongoing research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. Most of the online text recognition systems consist of three main phases which are preprocessing, feature extraction, and recognition phase. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in online recognition systems. Those techniques attempt to extract the feature vector of Arabic handwritten words, characters, numbers or strokes. This vector then will be fed into the recognition engine to recognize the pattern using the feature vector. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

An Anytime Algorithm for Camera-Based Character Recognition

Abstract: In a scene image, some characters are difficult to recognize and some others are recognized easily. Such difficult characters usually make the processing time long while easy characters are recognized in a short time. In this paper, we propose a system which recognizes each character with a proper cost for the difficulty. Through the process, easy characters are recognized early and difficult ones are recognized late. This is a desired property of an anytime algorithm that the recognition accuracy does not decrease as the time increases. In order to realize it, we propose a method which splits the recognition process into several times and accumulates the recognition results and extracted features. We also discuss what is required to realize the anytime algorithm for the scene character recognition task. Experiments reveal that the proposed method obtains recognition results of easy characters earlier than the conventional method.

Method and system for the spotting of arbitrary words in handwritten documents

Abstract: A method and system for the spotting of keywords in a handwritten document, the method comprising the steps of inputting an image of the handwritten document, performing word segmentation on the image to obtain segmented words, performing word matching, and outputting the spotted keywords. The word matching itself consisting in the sub-steps of performing character segmentation on the segmented words, performing character recognition on the segmented characters, performing distance computations on the recognized characters using a Generalized Hidden Markov Model with ergodic topology to identify words based on character models and performing non-keyword rejection using a classifier based on a combination of Gaussian Mixture Models, Hidden Markov Models and Support Vector Machines.

An efficient handwritten Devnagari character recognition system using neural network

Abstract: Character recognition systems for various languages and script has gain importance in recent decades and is the area of deep interest for many researchers. Their development is strongly integerated with Neural Networks. But, recognizing Devanagari Script is relatively greater challenge due to script's complexity. Various techniques have been implemented for this problem with many improvements so far. This paper describes the development and implementation of one such system comprising combination of several stages. Mainly Artificial Neural Network technique is used to designed to preprocess, segment and recognize devanagari characters. The system was designed, implemented, trained and found to exhibit an accuracy of 75.6% on noisy characters.

Visual Gesture Character String Recognition by Classification-Based Segmentation with Stroke Deletion

Abstract: The recognition of character strings in visual gestures has many potential applications, yet the segmentation of characters is a great challenge since the pen lift information is not available In this paper, we propose a visual gesture character string recognition method using the classification-based segmentation strategy In addition to the character classifier and character geometry models used for evaluating candidate segmentation-recognition paths, we introduce deletion geometry models for deleting stroke segments that are likely to be ligatures To perform experiments, we built a Kinect-based fingertip trajectory capturing system to collect gesture string data Experiments of digit string recognition show that the deletion geometry models improve the string recognition accuracy significantly The string-level correct rate is over 80%

Devanagari Text Recognition: A Transcription Based Formulation

Abstract: 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 segmentation module that can robustly work in presence of degraded (cut/fused) images and (ii) converting the outputs of the classifiers to a valid sequence of Unicodes. In this paper, we propose a formulation, where the expectations on these two modules is minimized, and the harder recognition task is modelled as learning of an appropriate sequence to sequence translation scheme. We thus formulate the recognition as a direct transcription problem. Given many examples of feature sequences and their corresponding Unicode representations, our objective is to learn a mapping which can convert a word directly into a Unicode sequence. This formulation has multiple practical advantages: (i) This reduces the number of classes significantly for the Indian scripts. (ii) It removes the need for a reliable word-to-symbol segmentation. (ii) It does not require strong annotation of symbols to design the classifiers, and (iii) It directly generates a valid sequence of Unicodes. We test our method on more than 6000 pages of printed Devanagari documents from multiple sources. Our method consistently outperforms other state of the art implementations.

Speech Recognition System and Isolated Word Recognition based on Hidden Markov Model (HMM) for Hearing Impaired

Abstract: The ability of a reader to recognize written words correctly, virtually and effortlessly is defined as Word Recognition or Isolated Word Recognition. It will recognize each word from their shape. Speech Recognition is the operating system which enablesto convert spoken words to written text which is called as Speech to Text (STT) method. Usual Method used in Speech Recognition (SR) is Neural Network, Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). The widely used technique for Speech Recognition is HMM. Hidden Markov Model assumes that successive acoustic features of a spoken word are state independent. The occurrence of one feature is independent of the occurrence of the others state. Here each single unit of word is considered as state. Based upon the probability of the state it generates possible word sequence for the spoken word. Instead of listening to the speech, the generated sequence of text can be easily viewed. Each word is recognized from their shape. People with hearing impaired can make use of this Speech Recognition.

“i” — A novel algorithm for optical character recognition (OCR)

Abstract: Computer vision, artificial intelligence and pattern recognition have been important areas of research for a while in the history of electronics and image processing. Optical character recognition (OCR) is one of the main aspects of computer vision and has evolved greatly since its inception. OCR is a method in which readable characters are recognized from optical data obtained digitally. Many methodologies and algorithms have been developed for this purpose using different approaches. Here we present one such approach for OCR named “ i ”. Amongst all other OCR systems available, “ i ” aims at a high speed, simple, font independent and size independent OCR system based on a unique segment extraction technique. This algorithm can be used as a kernel for single alphabet detection within a complete OCR solution system without the need for any complex mathematical operations. The highlight of this methodology is that, it does not use any libraries or databases of image matrices to recognize alphabets, but it has a unique algorithm to recognize alphabets instead. This algorithm has been implemented in MATLAB 7.14.0.739 build R2012a on a test set of 500 images of text and an accuracy of 100% for three font families namely Arial, Times New Roman and cchas been obtained.

Speech Recognition Server Integration Device and Speech Recognition Server Integration Method

Abstract: The speech recognition result through the general-purpose server and that through the specialized speech recognition server are integrated in an optimum manner, thereby, a speech recognition function least in errors in the end being provided. The specialized speech recognition server 108 is constructed with the words contained in the user dictionary data in use as well as the performance of the general-purpose speech recognition server 106 is preliminarily evaluated with such user dictionary data. Based on such evaluation result, information related to which recognition results through the specialized and general-purpose speech recognition servers are adopted and to how the adopted recognition results are weighted to obtain an optimum recognition result is preliminarily retained in the form of a database. Upon executing recognition, an optimum recognition result is obtained by comparing the recognition results through the specialized and general-purpose servers with the parameter for recognition result integration 118.

MDP Feature Extraction Technique for Offline Handwritten Gurmukhi Character Recognition

Abstract: Character recognition is intricate work because of the various writing styles of different individuals. Most of the published work on handwritten character recognition problems deals with statistical features, and a few works deal with structural features, in general, and Gurmukhi script, in particular. In the present work, we propose a methodology for offline handwritten Gurmukhi character recognition by using a modified division points (MDP) feature extraction technique. We also compare this technique with other recently used feature extraction techniques, namely zoning features, diagonal features, directional features, intersection and open end points features, and transition features. To select a representative set of features is the most significant task for a character recognition system. After feature extraction, the classification stage makes use of the features extracted in the previous stage to recognize the character. In this work, we used linear-support vector machines (linear-SVM), k-nearest neighbor (k-NN), and multilayer perceptron (MLP) classifiers for recognition. For experimental analysis, we used 10,500 samples of the isolated, offline, handwritten, basic 35 akhars of Gurmukhi script. The proposed system achieved a maximum recognition accuracy of 84.57%, 85.85% and 89.20% with linear-SVM, MLP and k-NN classifiers, respectively, with a five-fold cross validation technique.

A hybrid MLPNN/HMM recognition system for online Arabic Handwritten script

Abstract: Online Handwriting Recognition is still of interest with the big demand on the nomadic computers and the pen based interfaces. For the Arabic language, it is far to be claimed as a solved problem. This paper presents an online Arabic Handwriting Recognition System based on Hidden Markov Models (HMMs) and Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented to continuous strokes called segments based on the Beta-Elliptical strategy by inspecting the extremums points of the curvilinear velocity profile. A neural network trained with segment level contextual information is used to extract class character probabilities. The output of this network is decoded by HMMs to provide character level recognition. In evaluations on the ADAB database, we achieved 96.4% character recognition accuracy that is statistically significantly important in comparison with character recognition accuracies obtained from state-of-the-art online Arabic systems.

A System for Bangla Online Handwritten Text

Abstract: Recognition of Bangla compound characters has rarely got attention from researchers. This paper deals with segmentation and recognition of online handwritten Bangla cursive text containing basic and compound characters and all types of modifiers. Here, at first, we segment cursive words into primitives. Next primitives are recognized. A primitive may represent a character/compound character or a part of a character/compound character having meaningful structural information or a part incurred while joining two characters. We manually analyzed all the input texts written by different groups of people to create a ground truth set of distinct classes of primitives for result verification and we obtained 251 valid primitive classes. For automatic segmentation of text into primitives, we discovered some rules analyzing different joining patterns of Bangla characters. Applying these rules and using combination of online and offline information the segmentation technique was proposed. We achieved correct primitive segmentation rate of 97.89% from the 4984 online words. Directional features were used in SVM for recognition and we achieved average primitive recognition rate of 97.45%.

Novel Sub-character HMM Models for Arabic Text Recognition

Abstract: Hidden Markov Model (HMM) is one of the most widely used classifier for text recognition. In this paper we are presenting novel sub-character HMM models for Arabic text recognition. Modeling at sub-character level allows sharing of common patterns between different contextual forms of Arabic characters as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact and efficient recognizer with reduced model set and is expected to be more robust to the imbalance in data distribution. Experimental results using the sub-character model based recognition of handwritten Arabic text as well printed Arabic text are reported.

Handwritten digit recognition through wavelet decomposition and wavelet packet decomposition

Abstract: Handwritten digit recognition is a significant and established problem in computer vision and pattern recognition and a lot of research work has already been carried out in this area. In this paper a new technique for handwritten digit recognition is proposed. As the handwritten digits are not of the same size, thickness, style, position and orientation therefore different challenges have to be faced to resolve the problem of handwritten digit recognition. The uniqueness and variety in the writing styles of different people also influence the pattern and appearance of the digits. Handwritten digit recognition is the method of recognizing and classifying handwritten digits. It has wide application such as automatic processing of bank cheques, postal addresses and tax forms etc. In this paper, we present a wavelets analysis based technique for feature extraction. The task of classification is handled using KNN and SVM classifier. An overall high recognition rate of 97.04 is achieved on the test data set. The proposed scheme is tested on the well known MNIST data set.