Showing papers on "Handwriting recognition published in 1999"

Smart Sight: a tourist assistant system

Abstract: In this paper, we present our efforts towards developing an intelligent tourist system The system is equipped with a unique combination of sensors and software The hardware includes two computers, a GPS receiver, a lapel microphone plus an earphone, a video camera and a head-mounted display This combination includes a multimodal interface to take advantage of speech and gesture input to provide assistance for a tourist The software supports natural language processing, speech recognition, machine translation, handwriting recognition and multimodal fusion A vision module is trained to locate and read written language, is able to adapt to to new environments, and is able to interpret intentions offered by the user such as a spoken clarification or pointing gesture We illustrate the applications of the system using two examples

An omnifont open-vocabulary OCR system for English and Arabic

Abstract: We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus.

An HMM-based approach for off-line unconstrained handwritten word modeling and recognition

Abstract: Describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentation-symbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition.

A full English sentence database for off-line handwriting recognition

Abstract: We present a new database for off-line handwriting recognition, together with a few preprocessing and text segmentation procedures. The database is based on the Lancaster-Oslo/Bergen(LOB) corpus. This corpus is a collection of tests that were used to generate forms, which subsequently were filled out by persons in their own handwriting. As of December 1998 the database includes 556 forms produced by approximately 250 different writers. The database consists of full English sentences. It could serve as a basis for a variety of handwriting recognition tasks. The main focus, however is on recognition techniques that use linguistic knowledge beyond the lexicon level. This knowledge can be automatically derived from the corpus or it can be supplied from external sources.

Method and apparatus for immediate response handwriting recognition system that handles multiple character sets

Abstract: To efficiently recognize characters from several character sets, a palmtop computer system is disclosed wherein more that one character input area is displayed. Each character input area is designed to recognize strokes that represent characters from a different character set. In one embodiment, the palmtop computer system has an alphabetic input area and a numeral input area. In such an embodiment, strokes entered in the alphabetic input area are interpreted as alphabetic characters and strokes entered in the numeral input area are interpreted as numerals.

The IRESTE On/Off (IRONOFF) dual handwriting database

Abstract: Databases for character recognition algorithms are of fundamental interest for the training of statistics based recognition methods (neural networks, hidden Markov models) as well as for benchmarking existing recognition systems. Such databases currently exist, but none of them gives access to the online data (pen trajectory) and offline data (digital images) for the same writing signal. We have developed such a dual on/off database, named IRONOFF. Currently, it contains a large number of isolated characters, digits, and cursive words written by French writers. We have designed this database so that, given an online point, it can be mapped at the correct location in the corresponding scanned image, and conversely, each offline pixel can be temporally indexed. Since we think this database is of interest for a large part of the research community, it is publicly available.

Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries

Abstract: A method for image-based queries and search is proposed which is based on the generation of object outlines in images by using the pen, e.g., on color pen computers. The rationale of the approach is based on a survey on user needs, as well as on considerations from the point of view of pattern recognition and machine learning. By exploiting the actual presence of the human users with their perceptual-motor abilities and by storing textually annotated queries, an incrementally learning image retrieval system can be developed. As an initial test domain, sets of photographs of motor bicycles were used. Classification performances are given for outline and bitmap-derived feature sets, based on nearest-neighbour matching, with promising results. The benefit of the approach will be a user-based multimodal annotation of an image database, yielding a gradual improvement in precision and recall over time.

Analysis of class separation and combination of class-dependent features for handwriting recognition

Abstract: In this paper, we propose a new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities. In the second part, multiple feature vectors are combined to produce a new feature vector. Based on the fact that a feature has different discriminating powers for different classes, a new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.

A handwriting-based equation editor

Abstract: Current equation editing systems rely on either textbased equation description languages or on interactive construction by means of structure templates and menus. These systems are often tedious to use, even for experts, because the user is forced to “parse” the expressionsmentally before they are entered. This step is not normally part of the process of writing equations on paper or on a whiteboard. We describe a prototype equation editor that is based on handwriting recognition and automatic equation parsing. It is coupled with a user interface that incorporates a set of simple procedures for correcting errors made by the automatic interpretation. Although some correction by the user is typically necessary before the formula is recognized, we have found that the system is simpler and more natural to use than systems based on specialized languages or template-based interaction.

Chaincode contour processing for handwritten word recognition

Abstract: Contour representations of binary images of handwritten words afford considerable reduction in storage requirements while providing lossless representation. On the other hand, the one-dimensional nature of contours presents interesting challenges for processing images for handwritten word recognition. Our experiments indicate that significant gains are to be realized in both speed and recognition accuracy by using a contour representation in handwriting applications.

Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)

Abstract: The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen is a recent development in self-organizing map (SOM) computation. In this paper, we propose a method to realize ASSOM using a neural learning algorithm in nonlinear autoencoder networks. Our method has the advantage of numerical stability. We have applied our ASSOM model to build a modular classification system for handwritten digit recognition. Ten ASSOM modules are used to capture different features in the ten classes of digits. When a test digit is presented to all the modules, each module provides a reconstructed pattern and the system outputs a class label by comparing the ten reconstruction errors. Our experiments show promising results. For relatively small size modules, the classification accuracy reaches 99.3% on the training set and over 97% on the testing set.

A2iA Check Reader: a family of bank check recognition systems

Abstract: The paper presents new A2iA bank check recognition systems designed to process handwritten and/or printed checks issued in France, UK or USA. All the systems have identical architecture and design principles. However, each of them contains a country-specific part and is trained with country-specific data. Each system performs location, extraction, segmentation and recognition of courtesy and legal amounts in a document image, as well as deciding whether to accept or reject the check. The recognition rate is 80-90%. In the production mode, the check acceptance rate is 60-75%, with the misread rate corresponding to that of a human operator (close to 1%).

Handwriting recognition or reading? What is the situation at the dawn of the 3rd millenium?

Abstract: During the last forty years, Human Handwriting Processing (HHP) has most often been investigated under the frameworks of character (OCR) and pattern recognition. In recent years considerable progress has been made, and to date HHP can be viewed much more as an automatic Handwriting Reading (HR) task for the machine. In this paper we propose the use of handwriting invariants, a physical model for a first segmentation, a logical model for segmentation and recognition, a fundamental equation of handwriting, and to integrate several sources of perception and of knowledge in order to design Handwriting Reading Systems (HRS), which would be more universal systems than is currently the case. At the dawn of the 3rd millennium, we guess that HHP will be considered more as a perceptual and interpretation task requiring knowledge gained from studies on human language. This paper gives some guidelines and presents examples to design systems able to perceive and interpret, i.e., read, handwriting automatically.

Hybrid pen-input character recognition system based on integration of online-offline recognition

Abstract: Describes a handwritten character recognition system that integrates offline recognition requiring a bitmap image and online recognition involving an input pattern as a sequence of x-y coordinates. Offline recognition performs well for painted or overwritten patterns (for which online recognition would not be suited), whereas online recognition is suitable for very deformed patterns (for which offline recognition is not suited). Because each method has different recognition capabilities, the methods complement each other when integrated together. We have implemented a hybrid handwritten character recognition system in which the recognition results of the offline and online recognizer are integrated to create an improved product. After testing several integration methods for a handwritten character database, we found that the best method increased the recognition rate from 73.8% (offline) and 84.8% (online) to 87.6% (integrated).

Automatic separation of machine-printed and hand-written text lines

Abstract: There are many types of documents where machine-printed and hand-written texts appear intermixed. Since the optical character recognition (OCR) methodologies for machine-printed and hand-written texts are different, it is necessary to separate these two types of text before feeding them to the respective OCR systems. In this paper, we present such a scheme for both Bangla and Devnagari characters. The scheme is based on the structural and statistical features of the machine-printed and hand-written text lines. The classification scheme has an accuracy of about 98.3%.

System and method for providing user-directed constraints for handwriting recognition

Abstract: A data recognition system and method which allows a user to select between a “default recognition” mode and a “constrained recognition” mode via a user interface. In the default recognition mode, a recognition engine utilizes predetermined default recognition parameters to decode data (e.g., handwriting and speech). In the constrained recognition mode, the user can select one or more of a plurality of recognition constraints which temporarily modify the default recognition parameters to decode uncharacteristic and/or special data. The recognition parameters associated with the selected constraint enable the recognition engine to utilize specific information to decode the special data, thereby providing increased recognition accuracy.

A new comprehensive database of handwritten Arabic words, numbers, and signatures used for OCR testing

Abstract: This paper describes the formation of a comprehensive database of handwritten Arabic words, numbers, and signature, for use in optical character recognition research related to the Arabic language. So far no such (freely or commercially available) database exists.

Off-line signature recognition using parameterized Hough transform

Abstract: This article describes a method of an off-line signature recognition by using the Hough transform to detect stroke lines from the signature image. The Hough transform is used to extract the parameterized Hough space from the signature skeleton as a unique characteristic feature of signatures. In the experiment, the backpropagation neural network is used as a tool to evaluate the performance of the proposed method. The system has been tested with 70 test signatures from different persons. The experimental results reveal a recognition rate 95.24%.

Online Handwriting Recognition

Abstract: The sections in this article are 1 Handwriting 2 Data Acquisition and Preprocessing 3 Recognition 4 Postprocessing 5 Conclusion 6 Acknowledgments

Advances in Handwriting Recognition

Abstract: On-line handwriting recognition by discrete HMM with fast learning diacritical processing using efficient accounting procedures in a forward search a handwritten form reader architecture combining different classifiers and levels of knowledge - a first step towards an adaptive recognition system architecture for handwritten text recognition systems search algorithms for the recognition of cursive phrases without world segmentation a method for the determination of features used in human reading of cursive handwriting global methods for stroke segmentation an advanced segmentation technique for cursive word recognition document understanding based on maximum a posteriori probability estimation combining shape matrices and HMMs for hand-drawn pictogram recognition.

Recognition of English and Arabic numerals using a dynamic number of hidden neurons

Abstract: The paper introduces a method of finding the neighborhood of the optimal number of hidden neurons for an error backpropagation neural network with a single hidden layer. It is based on a study of the curvature of the error function, during the training phase of the network. The method assures convergence and bypasses local minimas. Experimental results show the uniqueness of the method's solution regardless of the initial values of the network's parameters. Two neural networks were built, one for recognizing unconstrained handwritten English numerals and the other for Arabic numerals. Recognition results and comparison with other methods are also presented.

Parsing and recognition of city, state, and ZIP codes in handwritten addresses

Abstract: In this paper we present a solution to the general vision problem of parsing and recognizing a set of correlated entities in the presence of imperfect information Our solution mechanism involves the generation of multiple hypotheses in the initial stages of the system, and the use of very-large vocabulary recognition, together with a database of all the valid combination of the correlated entities, to choose among the hypotheses We have applied our ideas and techniques to the specific task of identifying the city, state and zipcode fields in handwritten addresses Given the image of a handwritten address, our algorithm produces a ranking of the 76,121-entry database of valid (city, state, zip) triples in the US and in nearly 75% of the cases, the correct entry for the input address is assigned a rank of at most 10

Neural based handwritten character recognition

Abstract: The paper explores the existing ring based method (W.I. Reber, 1987), the new sector based method and the combination of these, termed the Fusion method for the recognition of handwritten English capital letters. The variability associated with the characters is accounted for by way of considering a fixed number of concentric rings in the case of the ring based approach and a fixed number of sectors in the case of the sector approach. Structural features such as end points, junction points and the number of branches are used for the preclassification of characters, the local features such as normalized vector lengths and angles derived from either ring or sector approaches are used in the training using the reference characters and subsequent recognition of the test characters. The recognition rates obtained are encouraging.

Signature classification by hidden Markov model

Abstract: Signature recognition is a relevant area in secure applications referred to as biometric identification. The image of the signature to be recognized (in off-line systems) can be considered as a spatio-temporal signal due to the shapely geometric and sequential character of the pencil drawing. The recognition and classification methods known to us are based on the extraction of geometric parameters and their classification by either a linear or nonlinear classifier. This procedure neglects the temporal information of the signature. In order to alleviate this, this paper proposes to use signature parameters with spatio-temporal information and its classification by a classifier capable of dealing with spatio-temporal problems as hidden Markov models (HMM). The proposed parameters are calculated in two stages; first, the preprocessing stage which includes noise reduction and outline detection through a skeletonization or thinning algorithm; and second, a parameterization stage in which the signature is encoded following the signature line and recording the length and direction of the pencil drawing obtaining a vector that includes the signature spatio-temporal information. The classification of the above parameters is done by a HMM classifier working in the same way as isolated word recognition systems. To design (train and test) the HMM classifier we have built a database of 24 signatures of 60 different writers.

A study of representations for pen based handwriting recognition of Tamil characters

Abstract: We study the important issue of choosing representations that are suitable for recognizing pen based handwriting of characters in Tamil, a language of India. Four different choices, based on the following set of features are considered: a sequence of directions and curvature; a sequence of angles; Fourier transform coefficients; and wavelet features. We provide arguments in support of the representation using wavelet features. A neural network designed using these features gives excellent accuracy for recognizing Tamil characters.