Showing papers on "Handwriting recognition published in 2008"

Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Abstract: Offline handwriting recognition—the automatic transcription of images of handwritten text—is a challenging task that combines computer vision with sequence learning. In most systems the two elements are handled separately, with sophisticated preprocessing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. By combining two recent innovations in neural networks—multidimensional recurrent neural networks and connectionist temporal classification—this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input. Unlike competing systems, it does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language. Evidence of its generality and power is provided by data from a recent international Arabic recognition competition, where it outperformed all entries (91.4% accuracy compared to 87.2% for the competition winner) despite the fact that neither author understands a word of Arabic.

A Novel Method of Combined Feature Extraction for Recognition

Abstract: Multimodal recognition is an emerging technique to overcome the non-robustness of the unimodal recognition in real applications. Canonical correlation analysis (CCA) has been employed as a powerful tool for feature fusion in the realization of such multimodal system. However, CCA is the unsupervised feature extraction and it does not utilize the class information of the samples, resulting in the constraint of the recognition performance. In this paper, the class information is incorporated into the framework of CCA for combined feature extraction, and a novel method of combined feature extraction for multimodal recognition, called discriminative canonical correlation analysis (DCCA), is proposed. The experiments show that DCCA outperforms some related methods of both unimodal recognition and multimodal recognition.

Online handwritten Bangla character recognition using HMM

Abstract: We describe here a novel scheme for recognition of online handwritten basic characters of Bangla, an Indian script used by more than 200 million people. There are 50 basic characters in Bangla and we have used a database of 24,500 online handwritten isolated character samples written by 70 persons. Samples in this database are composed of one or more strokes and we have collected all the strokes obtained from the training samples of the 50 character classes. These strokes are manually grouped into 54 classes based on the shape similarity of the graphemes that constitute the ideal character shapes. Strokes are recognized by using hidden Markov models (HMM). One HMM is constructed for each stroke class. A second stage of classification is used for recognition of characters using stroke classification results along with 50 look-up-tables (for 50 character classes).

Off-line Handwriting Text Line Segmentation : A Review

Abstract: Summary Text line segmentation is an essential pre-processing stage for off-line handwriting recognition in many Optical Character Recognition (OCR) systems. It is an important step because inaccurately segmented text lines will cause errors in the recognition stage. Text line segmentation of the handwritten documents is still one of the most complicated problems in developing a reliable OCR. The nature of handwriting makes the process of text line segmentation very challenging. Several techniques to segment handwriting text line have been proposed in the past. This paper seeks to provide a comprehensive review of the methods of off-line handwriting text line segmentation proposed by researchers.

Handwritten-Word Spotting Using Biologically Inspired Features

Abstract: For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired whole-word recognition method that is used to incrementally elicit word labels in a live Web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neurophysiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows us to classify text images that have a low frequency of occurrence. Typically, these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually, standard pattern-recognition technology cannot deal with these text images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

Arabic handwritten digit recognition

Abstract: In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time

A Complete Optical Character Recognition Methodology for Historical Documents

Abstract: In this paper a complete OCR methodology for recognizing historical documents, either printed or handwritten without any knowledge of the font, is presented. This methodology consists of three steps: The first two steps refer to creating a database for training using a set of documents, while the third one refers to recognition of new document images. First, a pre-processing step that includes image binarization and enhancement takes place. At a second step a top-down segmentation approach is used in order to detect text lines, words and characters. A clustering scheme is then adopted in order to group characters of similar shape. This is a semi-automatic procedure since the user is able to interact at any time in order to correct possible errors of clustering and assign an ASCII label. After this step, a database is created in order to be used for recognition. Finally, in the third step, for every new document image the above segmentation approach takes place while the recognition is based on the character database that has been produced at the previous step.

Efficient zone based feature extration algorithm for handwritten numeral recognition of four popular south indian scripts

Abstract: Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose Zone centroid and Image centroid based Distance metric feature extraction system. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. Similarly zone centroid is computed and average distance from the zone centroid to each pixel present in the zone is computed. We repeated this procedure for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 2*n such features are extracted. Nearest neighbor and Feed forward back propagation neural network classifiers are used for subsequent classification and recognition purpose. We obtained 99 %, 99%, 96% and 95 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

Online Handwritten Gurmukhi Character Recognition Using Elastic Matching

Abstract: This paper presents implementation of elastic matching technique to recognize online handwritten Gurmukhi characters. We have discussed a process that recognizes characters in two stages. First stage recognizes the strokes, in second stage, character is evaluated on the basis of recognized strokes. Feature are computed to strengthen recognition results. Also, we have discussed a simple way to store data for handwritten strokes and characters. The database for strokes stores script number, stroke number and stroke sample number for every point of a stroke. For 60 writer's and a set of 41 Gurmukhi characters, we have obtained recognition rate as 90.08%.

Offline Handwritten Devanagari Word Recognition: A Holistic Approach Based on Directional Chain Code Feature and HMM

Abstract: A hidden Markov model (HMM) based approach is proposed for recognition of offline handwritten Devanagari words. The histogram of chain-code directions in the image-strips, scanned from left to right by a sliding window, is used as the feature vector. A continuous density HMM is proposed to recognize a word image. In our approach the states of the HMM are not determined a priori, but are determined automatically based on a database of handwritten word images. A handwritten word image is assumed to be a string of several image frame primitives. These are in fact the states of the proposed HMM and are found using a certain mixture distribution. One HMM is constructed for each word. To classify an unknown word image, its class conditional probability for each HMM is computed. The class that gives highest such probability is finally selected.

Writer identification using edge-based directional probability distribution features for arabic words

Abstract: A system for writer identification based on Arabic handwritten words was built. First a database of words was gathered and used as a test base. Then, features vectors were extracted from writers' word images. Prior to feature extraction, normalization operations were applied to a word or text line. In this research, we studied the feature extraction and recognition operations on Arabic text, on the identification rate of writers. Since there is no well known database containing Arabic handwritten words for researchers to test, we built a new database of off-line Arabic handwriting text to be used for writer identification research. The proposed database is meant to provide training and testing sets for Arabic writer identification research. Arabic handwritten words were collected from 100 writers. We evaluated the performance of edge-based directional probability distributions as features and other features in Arabic writer identification.

White-space models for offline Arabic handwriting recognition

Abstract: We propose to explicitly model white-spaces for Arabic handwriting recognition within different writing variants. Position-dependent character shapes in Arabic handwriting allow for large white-spaces between characters even within words. Here, a separate character model for white-spaces in combination with a lexicon using different writing variants and character model length adaptation is proposed. Current handwriting recognition systems model the white-spaces implicitly within the character models leading to possibly degraded models, or try to explicitly segment the Arabic words into pieces of Arabic words being prone to segmentation errors. Several white-space modeling approaches are analyzed on the well known IFN/ENIT database and outperform the best reported error rates.

Writer Identification in Old Handwritten Music Scores

Abstract: The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.

Mathematics Instruction and the Tablet PC.

Abstract: The use of tablet PCs in teaching is a relatively new phenomenon. A cross between a notebook computer and a personal digital assistant (PDA), the tablet PC has all of the features of a notebook with the additional capability that the screen can also be used for input. Tablet PCs are usually equipped with a stylus that allows the user to write on the screen. Handwriting recognition software converts this input into text for use with software such as internet browsers and email programs. As an educational tool, two of the most important features of the tablet PC are annotation and wireless communication. The annotation feature allows the user to write on almost any document much as one would annotate a printout of the same document. The wireless communication feature allows tablet PCs to share information with one another. The advantages of these features and their impact on the Murray State University (MSU) classroom will be discussed in the evaluation section.

Handwriting Recognition Interface On A Device

Abstract: Methods, systems, and apparatus, including computer program products, for inputting text. A handwriting area is presented on a touch-sensitive display of a device. A handwritten input is received in the handwriting area. One or more candidates are identified for the handwritten input and presented. An input selecting one of the candidates is received. The selected candidate is presented as a current input in a text input area of the touch sensitive display.

The UJIpenchars Database: a Pen-Based Database of Isolated Handwritten Characters.

Abstract: The availability of large amounts of data is a fundamental prerequisite for building handwriting recognition systems. Any system needs a test set of labelled samples for measuring its performance along its development and guiding it. Moreover, there are systems that need additional samples for learning the recognition task they have to cope with later, i.e. a training set. Thus, the acquisition and distribution of standard databases has become an important issue in the handwriting recognition research community. Examples of widely used databases in the online domain are UNIPEN, IRONOFF, and Pendigits. This paper describes the current state of our own database, UJIpenchars, whose first version contains online representations of 1,364 isolated handwritten characters produced by 11 writers and is freely available at the UCI Machine Learning Repository. Moreover, we have recently concluded a second acquisition phase, totalling more than 11,000 samples from 60 writers to be made available in short as UJIpenchars2.

Offline handwriting recognition using genetic algorithm

Abstract: In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%. character recognition have already been published(II) but the method presented here is advanced than those methods since cursive handwriting can be recognized with the help of a combination of artificial neural networks and genetic algorithm, this becomes the primary advantage of the method over other existing methods. The methodology here has been developed with four multilayer artificial neural networks with Levenberg-Marquardt back propagation algorithm along with genetic algorithm unlike few published methods that use a multilayer feed- forward neural network(III) thus providing an efficient output as compared to the previously published works. The recent spurt in the advancement in handwriting recognition has provided publications one of which discussed here is recognition of text written in 'Oriya', a traditional south-eastern Indian language (IV) but do not involve any combinations of artificial neural networks and optimization techniques such as genetic algorithms which lead to lower efficiency in recognition as compared to the ones that our approach here presents. Several areas have seen application of neural networks such as the Processing of Verbs and Nouns (V), Face Detection (VI) and Real-time Face Detection (VII). The application of genetic algorithms in various areas like initial population generation methods (VIII), mooring pattern optimization (IX), substitution ciphers (X) and designing of reverse logistic networks (XI) has proved its advancement over its predecessors. The handwriting recognition model described here works at three stages, segmentation of the handwritten text, recognition of segmented characters with the help of artificial neural networks and lastly selecting the best solution from the four artificial neural network outputs with the help of genetic algorithm. The cursive handwriting recognition is carried out with the help of artificial neural networks, which is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation and which has the capability of being adaptive and thus can change its structure based on the information provided to it. The artificial neural networks made in this case contain 625 input neurons, 3 hidden layers and 26 output neurons. The recognition model involves the

Off-line Cursive Handwritten Tamil Character Recognition

Abstract: Concerning to optical character recognition, handwriting has sustained to persist as a means of communication and recording information in day to day life even with the introduction of new technologies. Hidden Markov Models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. A system for offline recognition of cursive handwritten Tamil characters is presented. In this effort, offline cursive handwritten recognition system for Tamil based on HMM and uses a combination of Time domain and frequency domain feature is proposed. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

A new radical-based approach to online handwritten Chinese character recognition

Abstract: This paper proposes a new radical-based approach for online handwritten chinese character recognition. The approach is novel in three respects: statistical classification of radicals, over-segmentation of characters into candidate radicals, and lexicon-driven recognition of characters. Currently, we have applied the approach to Chinese characters of left-right structure and are extending to other structures. Preliminary results on a sample set of 4,284 characters consisting of 1,118 radicals demonstrate the superiority of the proposed approach.

Reduced Dynamic Time Warping for Handwriting Recognition Based on Multidimensional Time Series of a Novel Pen Device

Abstract: The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has been applied for time series analysis of five different sensor channels providing pressure, acceleration and tilt data of the pen generated during handwriting on a paper pad. But the standard DTW has processing time and memory space problems which limit its practical use for online handwriting recognition. To face with this problem the DTW has been applied to the sum of the five sensor signals after an adequate down-sampling of the data. Preliminary results have shown that processing time and memory size could significantly be reduced without deterioration of performance in single character and word recognition. Further excellent accuracy in recognition was achieved which is mainly due to the reduced dynamic time warping RDTW technique and a novel pen device BiSP.

A Segmentation Based Approach to Offline Handwritten Devanagari Word Recognition

Abstract: The present paper proposes a segmentation-based approach to handwritten Devanagari word recognition. On the basis of the head line, a word image is segmented in to pseudo characters. Hidden Markov models are proposed to recognize the pseudo characters. The word level recognition is done on the basis of a string edit distance.

Neural network based handwritten numeral recognition of Kannada and Telugu scripts

Abstract: Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we present Zone and Distance metric based feature extraction system. The character centroid is computed and the image is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. This procedure is repeated for all the zones present in the numeral image. Finally n such features are extracted for classification and recognition. Feed forward back propagation neural network is designed for subsequent classification and recognition purpose. We obtained 98 % and 96 % recognition rate for Kannada and Telugu numerals respectively.

Word-based handwritten Arabic scripts recognition using DCT features and neural network classifier

Abstract: In this paper, a system is proposed for word-based recognition of handwritten Arabic scripts. Techniques are discussed in details in terms of three stages in the system, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, DCT features are extracted for each word sample. Finally, these features are then utilized to train a neural network for classification. The proposed system has been successfully tested on database (version v2.0p1e) consisting of 32492 Arabic words handwritten by more than 1000 different writers, and the results were promising and very encouraging.

Fractal-based system for Arabic/Latin, printed/handwritten script identification

Abstract: In this paper, we present multilingual automatic identification of Arabic and Latin in both handwritten and printed script. The proposed scheme is based, Firstly, on morphological transform of line text images, secondly on fractal analysis features of both (i): original texture of 2-D images, (ii): vertical and horizontal profile projection. We used two techniques to obtain only 12 features based on fractal multi-dimension. The proposed system has been tested for 1000 prototypes with various typefaces, scriptors styles and sizes. The accuracy discrimination rate is about of 96.64 % by using KNN, and 98.72 % by using RBF. Experimental results show the importance of the proposed approach.

Knowledge-Based Baseline Detection and Optimal Thresholding for Words Segmentation in Efficient Pre-Processing of Handwritten Arabic Text

Abstract: Techniques on detecting baseline and segmenting words in handwritten Arabic text are presented in this paper. Instead of using pure projection, knowledge of the location of the baseline is utilized for accurate baseline detection. Then, distances between words and subwords are respectively analyzed, and their statistical distributions are obtained to decide an optimal threshold in segmenting words. Results on IFN/ENIT database have validated our methods in terms of improved baseline detection and words segmentation for further recognition.

Handwriting symbol recognition accuracy using speech input

Abstract: Described is a bimodal data input technology by which handwriting recognition results are combined with speech recognition results to improve overall recognition accuracy. Handwriting data and speech data corresponding to mathematical symbols are received and processed (including being recognized) into respective graphs. A fusion mechanism uses the speech graph to enhance the handwriting graph, e.g., to better distinguish between similar handwritten symbols that are often misrecognized. The graphs include nodes representing symbols, and arcs between the nodes representing probability scores. When arcs in the first and second graphs are determined to match one another, such as aligned in time and associated with corresponding symbols, the probability score in the second graph for that arc is used to adjust the matching probability score in the first graph. Normalization and smoothing may be performed to correspond the graphs to one another and to control the influence of one graph on the other.

Isolated Handwritten Kannada and Tamil Numeral Recognition: A Novel Approach

Abstract: Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either online or off-line. There is a large demand for Optical character recognition on hand written documents. India is a multi-lingual country and multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we have proposed the projection distance metric and zoning based scheme for numeral recognition. We tested our proposed method for Kannada and Tamil numerals. A nearest neighbor classifier is used for subsequent purpose. The proposed method gives around 93% and 90% of recognition accuracy for Kannada and Tamil numerals respectively.