Showing papers on "Intelligent word recognition published in 1999"

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 handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance

Abstract: This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Video OCR: indexing digital new libraries by recognition of superimposed captions

Abstract: The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.

Method and system of handwritten-character recognition

Abstract: In accordance with the present character recognition method, an imaginary stroke is used to link from the ending point of each actual stroke of an input handwritten character to the starting point of the subsequent actual stroke thereof to form a single line. Then a feature level is detected for specifying the position of a turn of the single line and the direction and angle of the turn at the position. According to the detected position of the turn, the detected feature level is patterned on input mesh memories which are in turn compared with a previously formed dictionary mesh memory to calculate the resemblance of the input handwritten character to each handwritten character in a dictionary database. The handwritten character in the dictionary database that has the closest, calculated resemblance is recognized as the input handwritten character. According to the present method, an imaginary stroke added to an input handwritten character also allows correct recognition of a character with each stroke written cursively.

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.

Analysis of stroke structures of handwritten Chinese characters

Abstract: Most handwritten Chinese character recognition systems suffer from the variations in geometrical features for different writing styles. The stroke structures of different styles have proved to be more consistent than geometrical features. In an on-line recognition system, the stroke structure can be obtained according to the sequences of writing via a pen-based input device such as a tablet. But in an off-line recognition system, the input characters are scanned optically and saved as raster images, so the stroke structure information is not available. In this paper, we propose a method to extract strokes from an off-line handwritten Chinese character. We have developed four new techniques: 1) a new thinning algorithm based on Euclidean distance transformation and gradient oriented tracing, 2) a new line approximation method based on curvature segmentation, 3) artifact removal strategies based on geometrical analysis, and 4) stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Chinese character accurately and efficiently.

Modeling the prosody of hidden events for improved word recognition.

Abstract: We investigate a new approach for using speech prosody as a knowledge source for speech recognition. The idea is to penalize word hypotheses that are inconsistent with prosodic features such as duration and pitch. To model the interaction between words and prosody we modify the language model to represent hidden events such as sentence boundaries and various forms of disfluency, and combine with it decision trees that predict such events from prosodic features. N-best rescoring experiments on the Switchboard corpus show a small but consistent reduction of word error as a result of this modeling. We conclude with a preliminary analysis of the types of errors that are corrected by the prosodically informed model.

On-line handwritten formula recognition using hidden Markov models and context dependent graph grammars

Abstract: This paper presents an approach for the recognition of on-line handwritten mathematical expressions. The hidden Markov model (HMM) based system makes use of simultaneous segmentation and recognition capabilities, avoiding a crucial segmentation during pre-processing. With the segmentation and recognition results, obtained from the HMM recognizer it is possible to analyze and interpret the spatial two-dimensional arrangement of the symbols. We use a graph grammar approach for the structure recognition, also used in off-line recognition process, resulting in a general tree-structure of the underlying input-expression. The resulting constructed tree can be translated to any desired syntax (for example: Lisp, KT/sub E/X, and OpenMath).

Shape-based word recognition

Abstract: We describe a process of word recognition that has high tolerance for poor image quality, tunability to the lexical content of the documents to which it is applied, and high speed of operation. This process relies on the transformation of text images into character shape codes, and on special lexica that contain information on the shape of words. We rely on the structure of English and the high efficiency of mapping between shape codes and the characters in the words. Remaining ambiguity is reduced by template matching using exemplars derived from surrounding text, taking advantage of the local consistency of font, face and size as well as image quality. This paper describes the effects of lexical content, structure and processing on the performance of a word recognition engine. Word recognition performance is shown to be enhanced by the application of an appropriate lexicon. Recognition speed is shown to be essentially independent of the details of lexical content provided the intersection of the occurrences of words in the document and the lexicon is high. Word recognition accuracy is dependent on both intersection and specificity of the lexicon.

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).

Defining writer's invariants to adapt the recognition task

Abstract: Investigates the automatic reading of unconstrained omni-writer handwritten texts. This paper shows how to endow the reading system with adaptation faculties for each writer's handwriting. The adaptation principles are of major importance for making robust decisions when neither simple lexical nor syntactic rules can be used, e.g. for a free lexicon or for full text recognition. The first part of this paper defines the concept of writer's invariants. In the second part, we explain how the recognition system can be adapted to a particular handwriting by exploiting the graphical context defined by the writer's invariants. This adaptation is guaranteed, thanks to the writer's invariants, by activating interaction links over the whole text between the recognition procedures for word entities and those for letter entities.

Holistic verification of handwritten phrases

Abstract: In this paper, we describe a system for rapid verification of unconstrained off-line handwritten phrases using perceptual holistic features of the handwritten phrase image. The system is used to verify handwritten street names automatically extracted from live US mail against recognition results of analytical classifiers. Presented with a binary image of a street name and an ASCII street name, holistic features (reference lines, large gaps and local contour extrema) of the street name hypothesis are "predicted" from the expected features of the constituent characters using heuristic rules. A dynamic programming algorithm is used to match the predicted features with the extracted image features. Classes of holistic features are matched sequentially in increasing order of cost, allowing an ACCEPT/REJECT decision to be arrived at in a time-efficient manner. The system rejects errors with 98 percent accuracy at the 30 percent accept level, while consuming approximately 20/msec per image on the average on a 150 MHz SPARC 10.

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.

A new segmentation algorithm for handwritten word recognition

Abstract: An algorithm for segmenting unconstrained printed and cursive words is proposed The algorithm initially oversegments handwritten word images (for training and testing) using heuristics and feature detection An artificial neural network (ANN) is then trained with global features extracted from segmentation points found in words designated for training Segmentation points located in "test" word images are subsequently extracted and verified using the trained ANN Two major sets of experiments were conducted, resulting in segmentation accuracies of 7506% and 7652% The handwritten words used for experimentation were taken from the CEDAR CD-ROM The results obtained for segmentation can easily be used for comparison with other researchers using the same benchmark database

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.

Information capturing camera and developmental issues

Abstract: Presents a concept of an information capturing camera, which is an enhanced digital/video camera that can recognize characters in captured images and show relevant information, such as a translated version of the words in the image into another language. Also discussed are technical issues and possible approaches. Technical issues include detection of the text image regions, perspective distortion normalization, binarization of character images in unknown colors, recognition of omnifont and decorative characters, word recognition and noun compound translation. Finally, an international collaboration on the development of the required technology is proposed.

Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database

Abstract: A new intelligent segmentation technique is proposed that may be used in conjunction with a neural classifier and a simple lexicon for the recognition of difficult handwritten words. A heuristic segmentation algorithm is initially used to over-segment each word. An artificial neural network (ANN) trained with 32,034 segmentation points is then used to verify the validity of the segmentation points found. Following segmentation, character matrices from each word are extracted, normalised and then passed through a global feature extractor, after which a second ANN trained with segmented characters is used for classification. These recognised characters are grouped into words and presented to a variable-length lexicon that utilises a string processing algorithm to compare and retrieve those words with the highest confidences. This research provides promising results for segmentation, character and word recognition.

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.

Off-line handwritten word recognition using hidden Markov models

Abstract: 1 Centre for Pattern Recognition and Machine Intelligence Department of Computer Science, Concordia University 1455 de Maisonneuve Boulevard West Suite GM-606, Montreal, Canada H3G 1M8 2 Ecole de Technologie Superieure Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA) 1100 Notre-Dame Ouest, Montreal, Canada H3C 1K3 3 Service de Recherche Technique de La Poste Departement Reconnaissance, Modelisation Optimisation (RMO) 10, rue de l’ile Mâbon, 44063 Nantes Cedex 02, France 4 Departamento de Informatica (Computer Science Department) Pontificia Universidade Catolica do Parana Av. Imaculada Conceicao, 1155 Prado Velho 80.215-901 Curitiba PR BRAZIL

Knowledge-based intelligent techniques in character recognition

Abstract: An Introduction to Character Recognition - L.C. Jain and B. Lazzerini Recognition of Handwritten Digits in the Real World by a Neocognitron - H. Shouno, K. Fukushima and M. Okada Recognition of Rotated Patterns Using Neocognitron - S. Satoh, J. Kunoiwa, H. Aso and S. Miyuke Soft Computing Approach to Hand-written Numeral Recognition - J. F. Baldwin, T. P. Martin, and O. Stylianidis Handwritten Character Recognition Using an MLP - F. Sorbello, G. A. M. Gioiello, and S. Vitabile Signature Verification Based on Fuzzy Genetic Algorithm - J. N. K. Liu, and G. S. K. Fung Application of a Generic Neural Network to Handwritten Digit Classification - D. S. Banarse and A. Duller High-speed Recognition of Handwritten Amounts On Italian Checks - B. Lazzerini, L. M. Reyneri , F. Gregoretti, and A. Mariani Off-line Handwritten Word Recognition Using Hidden Markov Models - A. El-Yacoubi, R. Sabourin, M. Gilloux and C. Y. Suen Off-line Handwriting Recognition with Context Dependent Fuzzy Rules - A. Malaviya, F. Ivancic, J. Balasubramaniam and L. Peters License-plate Recognition - M. H. Brugge, J. A. G. Nijihuis, L. Spaanenburg, and J. H. Stevens Index

Postprocessing statistical language models for handwritten Chinese character recognizer

Abstract: Two statistical language models have been investigated on their effectiveness in upgrading the accuracy of a Chinese character recognizer. The baseline model is one of lexical analytic nature which segments a sequence of character images according to the maximum matching of words with consideration of word binding forces. A model of bigram statistics of word-classes is then investigated and compared against the baseline model in terms of recognition rate improvement on the image recognizer. On the average, the baseline language model improves the recognition rate by about 7% while the bigram statistics model upgrades it by about 10%.

Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data

Abstract: The paper deals with the performance evaluation of a novel hybrid approach to large vocabulary cursive handwriting recognition and contains various innovations. 1) It presents the investigation of a new hybrid approach to handwriting recognition, consisting of hidden Markov models (HMMs) and neural networks trained with a special information theory based training criterion. This approach has only been recently introduced successfully to online handwriting recognition and is now investigated for the first time for offline recognition. 2) The hybrid approach is extensively compared to traditional HMM modeling techniques and the superior performance of the new hybrid approach is demonstrated. 3) The data for the comparison has been obtained from a database containing online handwritten data which has been converted to offline data. Therefore, a multiple evaluation has been carried out, incorporating the comparison of different modeling techniques and the additional comparison of each technique for online and offline recognition, using a unique database. The results confirm that online recognition leads to better recognition results due to the dynamic information of the data, but also show that it is possible to obtain recognition rates for offline recognition that are close to the results obtained for online recognition. Furthermore, it can be shown that for both online and offline recognition, the new hybrid approach clearly outperforms the competing traditional HMM techniques. It is also shown that the new hybrid approach yields superior results for the offline recognition of machine printed multifont characters.

Lexicon-driven handwritten word recognition using optimal linear combinations of order statistics

Abstract: In the standard segmentation-based approach to handwritten word recognition, individual character-class confidence scores are combined via averaging to estimate confidences in the hypothesized identities for a word. We describe a methodology for generating optimal linear combination of order statistics operators for combining character class confidence scores. Experimental results are provided on over 1000 word images.

On-line handwritten Chinese character recognition apparatus

Abstract: An on-line handwritten Chinese character recognition apparatus based on character shapes uses a reference file for look-up of compressed codes-sequence codes in a conventional input method founded on dismantling by character shapes as reference when comparing character to reduce the number of matching templates required in the on-line handwritten Chinese character recognition system and to lower the time for matching. Input handwritten Chinese characters are dismantled into constituting radicals. Thereafter, with reference to code retrieving rules of the input method, and based on the character composing method of handwritten characters, radicals for comparison are retrieved. Then, the compression formula of the input method is used to compress the codes of the base radicals, and the compressed codes are compared with contents of the input method reference file to obtain the sequence codes of candidate characters. Finally, the final recognized sequence code is decided via further comparison during post-processing. The character that corresponds to the sequence code in a character font file is the recognition result and is outputted.

Handwritten digit recognition based on multi-classifier combination

Abstract: A handwritten digit recognition method based on multi-classifier combination is described in the paper. First I an objective parameter called performance parameter is defined to judge the classifier's performance. Then, a new combination algorithm of multi-classifier is presented. Some properties of combination method are also given in this paper. The Concordia University CENPARMI handwritten digit database is used in the experiment. Nine classifiers which use different features are combined to recognize the image. The experiment results (Recognized,Rejected and Reliability) are 97.05%, 2.05%, 99.08% respectively.

A HMM–based System for Recognition of Handwritten Address Words

Abstract: This paper introduces a new handwriting recognition system that is currently under development. Our application is the reading of German handwritten addresses for automatic mail sorting. The quality of the handwritten words is often bad in this application, because writers are not very cooperative. Therefore we have developed some suitable and eecient preprocessing operations to clean the image and normalize the writing. Because the words are often diicult to segment into letters, we have chosen a segmentation{free approach for recognition with semi{continuous Hidden Markov Models. We are applying the technique of context modelling in a model hierarchy in order to train more speciic letter models. For training and evaluation, we have used a large sample of 15000 handwritten city and street names. A number of experiments have been performed to evaluate strategies for feature space reduction (Karhunen{Loeve transform, linear discrim-inant analysis). On a 100 word lexicon, we achieve recognition rates of up to 90% on large independent test sets.

Network-based approach to online cursive script recognition

Abstract: The idea of combining the network of HMMs and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra computation is required.