Showing papers on "Handwriting recognition published in 2009"

A Novel Connectionist System for Unconstrained Handwriting Recognition

Abstract: Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.

Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals

Abstract: This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.

Markov models for offline handwriting recognition: a survey

Abstract: Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-model-based offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

Error correction using fact repositories

Abstract: The disclosed system and method apply stores of factual information to correct errors in digital text, for example, generated from OCR, speech and/or handwriting recognition devices, and other automatic recognition devices. A text produced by OCR, speech recognition, handwriting recognition, and others may be processed to extract discussed facts. Databases of facts are searched based on information in the text. After comparing facts asserted in the text with the factual data from the databases, suggested corrections of the text are produced.

Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition

Abstract: The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.

ICDAR 2009 Handwriting Segmentation Contest

Abstract: This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and procedures to record recent advances in off-line handwriting segmentation. Two benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare all submitted algorithms as well as some state-of-the-art methods for handwritten document image segmentation in realistic circumstances. Handwritten document images were produced by many writers in two Latin based languages (English and Greek) and in one Indian language (Bangla, the second most popular language in India). These images were manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation results. The datasets of previously organized contests (ICDAR2007, ICDAR2009 and ICFHR2010 Handwriting Segmentation Contests) along with a dataset of Bangla document images were used as training dataset. Eleven methods are submitted in this competition. A brief description of the submitted algorithms, the evaluation criteria and the segmentation results obtained from the submitted methods are also provided in this manuscript.

A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines

Abstract: In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images.

Large margin training for hidden Markov models with partially observed states

Abstract: Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the non-convexity of the optimization problem, previous works usually rely on severe approximations so that it is still an open problem. We propose a new learning algorithm that relies on non-convex optimization and bundle methods and allows tackling the original optimization problem as is. It is proved to converge to a solution with accuracy e with a rate O (1/e). We provide experimental results gained on speech and handwriting recognition that demonstrate the potential of the method.

ICDAR 2009 Handwriting Recognition Competition

Abstract: This paper describes the handwriting recognition competitionheld at ICDAR 2009. This competition is based onthe RIMES-database, with French written text documents.These document are classified in three different categories,complete text pages, words, and isolated characters. Thisyear 10 systems were submitted for the handwritten recognitioncompetition on snippets of French words. The systemswere evaluated in three subtask depending of the sizes ofthe used dictionary. A comparison between different classificationand recognition systems show interesting results. Ashort description of the participating groups, their systems,and the results achieved are presented.

Comparative Study of Devnagari Handwritten Character Recognition Using Different Feature and Classifiers

Abstract: In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images.

Handwritten Text Line Segmentation by Shredding Text into its Lines

Abstract: In this paper, we propose a novel technique to segment handwritten document images into text lines by shredding their surface with local minima tracers. Our approach is based on the topological assumption that for each text line, there exists a path from one side of the image to the other that traverses only one text line. We first blur the image and then use tracers to follow the white-most and black-most paths from left to right as well as from right to left in order to shred the image into text line areas. We experimentally tested the proposed methodology and got promising results comparable to state of the art text line segmentation techniques.

Automatic Transcription of Handwritten Medieval Documents

Abstract: The automatic transcription of historical documents is vital for the creation of digital libraries. In order to make images of valuable old documents amenable to browsing, a transcription of high accuracy is needed. In this paper, two state-of-the art recognizers originally developed for modern scripts are applied to medieval documents. The first is based on Hidden Markov Models and the second uses a Neural Network with a bidirectional Long Short-Term Memory. On a dataset of word images extracted from a medieval manuscript of the 13th century, written in Middle High German by several writers, it is demonstrated that a word accuracy of 93.32% is achievable. This is far above the word accuracy of 77.12% achieved with the same recognizers for unconstrained modern scripts written in English. These results encourage the development of real world systems for automatic transcription of historical documents with a view to image and text browsing in digital libraries.

Results of the RIMES Evaluation Campaign for Handwritten Mail Processing

Abstract: This paper presents the results of the second test phase of the RIMES evaluation campaign. The latter is the first large-scale evaluation campaign intended to all the key players of the handwritten recognition and document analysis communities. It proposes various tasks around recognition and indexing of handwritten letters such as those sent by postal mail or fax by individuals to companies or administrations. In this second evaluation test, automatic systems have been evaluated on three themes: layout analysis, handwriting recognition and writer identification. The databases used are part of the RIMES database of 5605 real mails completely annotated as well as secondary databases of isolated characters and handwritten words (250,000 snippets). The paper reports on protocols and gives the results obtained in the campaign.(RIMES : Reconnaissance et Indexation de donnees Manuscrites et de fac similES / Recognition and Indexing of handwritten documents and faxes)

Script-Independent Handwritten Textlines Segmentation Using Active Contours

Abstract: Handwritten document images contain textlines with multi orientations, touching and overlapping characters within consecutive textlines, and small inter-line spacing making textline segmentation a difficult task. In this paper we propose a novel, script-independent textline segmentation approach for handwritten documents, which is robust against above mentioned problems. We model textline extraction as a general image segmentation task. We compute the central line of parts of textlines using ridges over the smoothed image. Then we adapt the state-of-the-art active contours (snakes) over ridges, which results in textline segmentation. Unlike the ``Level Set'' and "Mumford-Shah model'' based handwritten textline segmentation methods, our method use matched filter bank approach for smoothing and does not require heuristic post processing steps for merging or splitting segmented textlines. Experimental results prove the effectiveness of the proposed algorithm. We evaluated our algorithm on ICDAR 2007 handwritten segmentation contest dataset and obtained an accuracy of 96.3%.

A Set of Chain Code Based Features for Writer Recognition

Abstract: This communication presents an effective method for writer recognition in handwritten documents. We have introduced a set of features that are extracted from the contours of handwritten images at different observation levels. At the global level, we extract the histograms of the chain code, the first and second order differential chain codes and, the histogram of the curvature indices at each point of the contour of handwriting. At the local level, the handwritten text is divided into a large number of small adaptive windows and within each window the contribution of each of the eight directions (and their differentials) is counted in the corresponding histograms. Two writings are then compared by computing the distances between their respective histograms. The system trained and tested on two different data sets of 650 and 225 writers respectively, exhibited promising results on writer identification and verification.

Combining Multiple HMMs Using On-line and Off-line Features for Off-line Arabic Handwriting Recognition

Abstract: This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov Models classifiers. Beside the classical use of the off-line features, we add the use of on-line features and the combination of the developed systems. The designed recognizer is implemented using the HMM-Toolkit. In a first step, we use different features to make the classification and we compare the performance of single classifiers. In a second step, we proceed to the combination of the on-line and the off-line based systems using different combination methods. The system is evaluated using the IFN/ENIT database. The recognition rate is in maximum 63.90% for the individual systems. The combination of the on-line and the off-line systems allows to improve the system accuracy to 81.93% which exceeds the best result of the ICDAR 2005 competition.

Method and apparatus for using pressure information for improved computer controlled handwriting recognition, data entry and user authentication

Abstract: A method and system utilizing both (x, y) coordinate (“spatial”) stroke data and associated pressure information for improved handwriting recognition. The method and system can also be applied to all types of handwriting-based data entry applications and also to user authentication. The digitizer pad used in the computer system gives both spatial information and associated pressure data when a stroke is being drawn thereon, e.g., by a stylus. Pressure information can be used to differentiate between different character sets, e.g., upper case and lower case characters for certain alphabetic characters. The spatial stroke data then identifies the particular character. The pressure information can also be used to adjust any display attribute, such as character font size, font selection, color, italic, bold, underline, shadow, language, etc. The associated pressure information can also be used for recognizing a signature. In this case, a user is allowed to sign a name on the digitizer pad. This provides non-character based user authentication that relies not only on the spatial stroke data but also on the pressure applied at different points in the signed name or image. Pressure information can also be used to provide improved handwriting-based data entry. For instance, in a drafting program, the pressure of a drawn line can be used to determine its width. Generally, pressure data can also be used to improve handwriting recognition tasks and heuristics.

ICDAR 2009 Online Arabic Handwriting Recognition Competition

Abstract: This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which is unknown to all participants (set 4). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.

On-line Arabic handwriting recognition with templates

Abstract: After a long period of focus on western and East Asian scripts there is now a general trend in the on-line handwriting recognition community to explore recognition of other scripts such as Arabic and various Indic scripts. One difficulty with the Arabic script is the number and position of diacritic marks associated to Arabic characters. This paper explores the application of a template matching scheme to the recognition of Arabic script with a novel algorithm for dynamically treating the diacritical marks. Template based systems are robust to conditions with scarce training data and in experiments the proposed system outperformed a reference system based on the promising state-of-the-art network technique of BLSTM. Experiments have been conducted in an environment similar to that of many handheld devices with promising results both in terms of memory consumption and response time.

Line Segmentation for Degraded Handwritten Historical Documents

Abstract: We propose a novel approach for text line segmentation based on adaptive local projection profiles. Our algorithm is suitable for degraded documents with text lines written in large skew. The main novelty of our approach is applying the local algorithm in an incremental manner that adapts to the skew of each text line as it progresses. The proposed approach achieves very accurate results on a set of degraded documents with lines written in different skew angles and curvatures.

HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition

Abstract: In this paper, we present a large scale off-line handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinesecharacters written by 1,000 different subjects. The writers’ information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose.

Towards Handwritten Mathematical Expression Recognition

Abstract: In this paper, we propose a new framework for online handwritten mathematical expression recognition. The proposed architecture aims at handling mathematical expression recognition as a simultaneous optimization of symbol segmentation, symbol recognition, and 2D structure recognition under the restriction of a mathematical expression grammar. To achieve this goal, we consider a hypothesis generation mechanism supporting a 2D grouping of elementary strokes, a cost function defining the global likelihood of a solution, and a dynamic programming scheme giving at the end the best global solution according to a 2D grammar and a classifier. As a classifier, a neural network architecture is used; it is trained within the overall architecture allowing rejecting incorrect segmented patterns. The proposed system is trained with a set of synthetic online handwritten mathematical expressions. When tested on a set of real complex expressions, the system achieves promising results at both symbol and expression interpretation levels.

A New Large Urdu Database for Off-Line Handwriting Recognition

Abstract: A new large Urdu handwriting database, which includes isolated digits, numeral strings with/without decimal points, five special symbols, 44 isolated characters, 57 Urdu words (mostly financial related), and Urdu dates in different patterns, was designed at Centre for Pattern Recognition and Machine Intelligence (CENPARMI). It is the first database for Urdu off-line handwriting recognition. It involves a large number of Urdu native speakers from different regions of the world. Moreover, the database has different formats --- true color, gray level and binary. Experiments on Urdu digits recognition has been conducted with an accuracy of 98.61%. Methodologies in image pre-processing, gradient feature extraction and classification using SVM have been described, and a detailed error analysis is presented on the recognition results.

Recognition of Handwriting from Electromyography

Abstract: Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear.

Arabic Handwriting Recognition Using Restored Stroke Chronology

Abstract: In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic informations we make a sampling operation by the consideration of trajectory curvatures. We proceed to calculate the curvilinear velocity signal and use the beta-elliptical modelling which is developed in on-line systems to calculate other characteristics. Our approach is validated by Hmm Tool Kit (HTK) recognition system using IFN/ENIT database.