Showing papers on "Handwriting recognition published in 1997"

A lexicon driven approach to handwritten word recognition for real-time applications

Abstract: A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi.

Handwriting recognition system and method

Abstract: A system for recognizing handwritten characters, including pre-processing apparatus for generating a set of features for each handwritten character, a neural network disposed for operating on sparse data structures of those features and generating a set of confidence values for each possible character symbol which might correspond to the handwritten character, and post-processing apparatus for adjusting those confidence values and for selecting a character symbol consistent with external knowledge about handwritten characters and the language they are written in. The pre-processing apparatus scales and re-parameterizes the handwritten strokes, encodes the scaled and re-parameterizd strokes into fuzzy membership vectors and binary pointwise data, and combines the vectors and data into a sparse data structure of features. The (nonconvolutional) neural network performs a matrix-vector multiply on the sparse data structure, using only the data for nonzero features collected in that structure, and, for a first layer of that neural network, using only successive chunks of the neural weights. The post-processing apparatus adjusts the confidence values for character symbols using a set of expert rules embodying common-sense knowledge, from which it generates a set of character probabilities for each character position; these character probabilities are combined with a Markov model of character sequence transitions and a dictionary of known words, to produce a final work output for a sequence of handwritten characters.

Methods and apparatuses for handwriting recognition

Abstract: Method and apparatus for handwriting recognition system for ideographic characters and other characters based on subcharacter hidden Markov models. The ideographic characters are modeled using a sequence of subcharacter models and by using two-dimensional geometric layout models of the subcharacters. The subcharacter hidden Markov models are created according to one embodiment by following a set of design rules. The combination of the sequence and geometric layout of the subcharacter models is used to recognize the handwriting character.

Combining multiple representations and classifiers for pen-based handwritten digit recognition

Abstract: We investigate techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time. We compare multiexpert and multistage combination techniques and discuss in detail in a comparative manner methods for combining multiple learners: voting, mixture of experts, stacking, boosting and cascading. In pen based handwritten character recognition, the input is the dynamic movement of the pentip over the pressure sensitive tablet. There is also the image formed as a result of this movement. On a real world database, we notice that the two multi layer perceptron (MLP) neural network based classifiers using these representations separately make errors on different patterns, implying that a suitable combination of the two would lead to higher accuracy. Thus we implement and compare voting, mixture of experts, stacking and cascading. Combined classifiers have an error percentage less than individual ones. The final combined system of two MLPs has less complexity and memory requirement than a single k nearest neighbor using one of the representations.

Neural and fuzzy methods in handwriting recognition

Abstract: Handwriting recognition requires tools and techniques that recognize complex character patterns and represent imprecise, common-sense knowledge about the general appearance of characters, words and phrases. Neural networks and fuzzy logic are complementary tools for solving such problems. Neural networks, which are highly nonlinear and highly interconnected for processing imprecise information, can finely approximate complicated decision boundaries. Fuzzy set methods can represent degrees of truth or belonging. Fuzzy logic encodes imprecise knowledge and naturally maintains multiple hypotheses that result from the uncertainty and vagueness inherent in real problems. By combining the complementary strengths of neural and fuzzy approaches into a hybrid system, we can attain an increased recognition capability for solving handwriting recognition problems. This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.

Automatic Bankcheck Processing: A New Engineered System

Abstract: A new bankcheck processing system is presented in this paper. A full exploitation of the contextual knowledge, together with a multi-expert approach, have been used both to analyze the complex shape of handwritten text and to design the system. Several processing modules have been integrated in the system. Some of the most relevant are those for data acquisition, preprocessing, machine-printed numeral recognition, layout analysis, courtesy amount recognition, legal amount recognition, amount validation, and signature verification. Some combination techniques have also been used in the system. Reuse and maintenance of the system were two of the main goals of the designing process and the Khoros software tool was used for this purpose.

An evolutionary neuro-fuzzy approach to recognize on-line Arabic handwriting

Abstract: The author describes a system that recognizes on-line Arabic cursive handwriting. In this system, a genetic algorithm is used to select the best combination of characters recognized by a fuzzy neural network. The handwritten words used in this system are modelled by a theory of movement generation. Based on this motor theory, the features extracted from each character are the neuro-physiological and biomechanical parameters of the equation describing the curvilinear velocity of the script. The evolutionary approach proposed permits the recognition of cursive handwriting with a segmentation procedure allowing overlapped strokes having neuro-physiological meaning.

Data sets for ocr and document image understanding research

Abstract: Several significant sets of labeled samples of image data are surveyed that can be used in the development of algorithms for offline and online handwriting recognition as well as for machine printed text recognition. The method used to gather each data set, the numbers of samples they contain, and the associated truth data are discussed. In the domain of offline handwriting, the CEDAR, NIST, and CENPARMI data sets are pre­ sented. These contain primarily isolated digits and alphabetic characters. The UNIPEN data set of online handwriting was collected from a number of independent sources and it contains individual characters as well as handwritten phrases. The University of Wash­ ington document image databases are also discussed. They contain a large number of English and Japanese document images that were selected from a range of publications.

Off line Arabic character recognition: a survey

Abstract: Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic characters because of the complexity of printed and handwritten text, and this problem is still an open research field. The main objective of this paper is to present the state of Arabic character recognition research throughout the last two decades.

A new recurrent neural-network architecture for visual pattern recognition

Abstract: We propose a new type of recurrent neural-network architecture, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks with respect to function and architecture, because it has been originally extended from being a mere multilayer feedforward neural network, to improve discrimination and generalization powers. We also prove the convergence properties of the learning algorithm in the proposed recurrent neural network, and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeric database of Concordia University, Montreal, Canada. Experimental results have confirmed that the proposed recurrent neural network improves discrimination and generalization powers in the recognition of visual patterns.

Hybrid fuzzy-neural systems in handwritten word recognition

Abstract: Two hybrid fuzzy neural systems are developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class membership values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low membership values to a wide variety of noncharacter segments resulting from erroneous segmentations. Each hybrid is a cascaded system. The first stage of both is a self-organizing feature map (SOFM). The second stages map distances into membership values. The third stage of one system is a multilayer perceptron (MLP). The third stage of the other is a bank of Choquet fuzzy integrals (FI). The two systems are compared individually and as a combination to the baseline system. The new systems each perform better than the baseline system. The MLP system slightly outperforms the FI system, but the combination of the two outperforms the individual systems with a small increase in computational cost over the MLP system. Recognition rates of over 92% are achieved with a lexicon set having average size of 100. Experiments were performed on a standard test set from the SUNY/USPS CD-ROM database.

A neuro-fuzzy approach to recognize Arabic handwritten characters

Abstract: In this paper we describe a system that recognizes online Arabic handwritten characters. In this system, a fuzzy neural network is used to classify characters. The characters used in this system were segmented from cursive handwriting that are modelled by a theory of movement generation. Based on this theory, the features extracted from each character are the neuro-physiological parameters of the equation describing the curvilinear velocity of the script. For each character presented to the system, a fuzzy membership is assigned to each output of the neural network.

HMM word recognition engine

Abstract: We describe a hidden Markov model (HMM) based word recognition engine being developed to be integrated with the CENPARMI bank cheque processing system. The various modules are described in detail, and preliminary results are compared with our previous global feature recognition scheme. The engine is tested on words from a database of over 4,500 cheques of 1,400 writers.

Apparatus and method for presenting and gathering text entries in a pen-based input device

Abstract: A method and apparatus for presenting and gathering text entries in a pen-based input device (130). The apparatus and method allows for the entry of textual information (160) into a computing device using either handwriting recognition (740), character selection (220), or expression selection (760). Individual entry and selection fields (220, 240, 310) for each of these methods are provided to the user in a coordinated comprehensive method of displaying and gathering the textual information. Handwriting recognition functionality is used to facilitate character recognition. Furthermore, lists of expressions allow the text entry method and apparatus to anticipate the next character (250) or expression (315) to be entered by the user.

Logo and word matching using a general approach to signal registration

Abstract: The paper presents work in the field of logo and word recognition. The approach is based on a general theory for signal registration and is thus applicable to a broad variety of signal processing domains. It has been fruitfully applied to solve speech and handwriting recognition as well as tasks in the field of document analysis.

Perceptual model of handwriting drawing. Application to the handwriting segmentation problem

Abstract: A new handwriting modeling and segmentation approach is introduced for cursive letter and word analysis. For the letter analysis, the proposed method is based on the detection of a set of "perceptual anchorage points" to extract a priori pertinent strokes. This physical segmentation of the handwritten drawing enables us to conduct a logical modeling of letters with respect to the most stable strokes of each letter class. For the handwritten word analysis, we present a constructive segmentation approach to overcome the word segmentation problem. The main idea is to locate "anchorage structures" in the word drawing based on the most robust strokes of the letters. This new approach of handwriting analysis has been implemented in a writer-independent online handwriting recognition system. Experimental results are reported using a lexicon context of 1128, 7000 and 25,000 words.

Bankcheck recognition using cross validation between legal and courtesy amounts

Abstract: A bankcheck reading system using cross validation of both the legal and the courtesy amounts is presented in this paper. Some of the challenges posed by the task are (i) segmentation of the legal amount into words, (ii) location of boundaries between dollars and cents amounts, and (iii) high accuracy in terms of recognition performance. Word segmentation in the legal amount is a serious issue because of the nature of the data and patrons' writing habits which tend to clump words together. We have developed a word segmentation algorithm based on the character segmentation results to address this issue. The list of possible amounts generated by the word segmentation hypotheses is used as lexicon for the courtesy amount recognition. The order of magnitude of the amount is estimated during legal amount recognition. We treat the courtesy amount as a numeral string and apply the same word recognition scheme as used for the legal amount. Our approach to check recognition differs from traditional methods in two significant aspects: First, our emphasis on both the legal and the courtesy amounts is balanced. We use an accurate word recognizer which performs equally well on alpha words and digit strings. Second, our combination strategy is serial rather than the commonly used parallel method. Experimental results show that 43.8% of check images are correctly read with an error rate of 0%.

A structural and relational approach to handwritten word recognition

Abstract: In this paper, we present a new off-line word recognition system that is able to recognize unconstrained handwritten words using grey-scale images. This is based on structural and relational information in the handwritten word. We use Gabor filters to extract features from the words, and then use an evidence-based approach for word classification. A solution to the Gabor filter parameter estimation problem is given, enabling the Gabor filter to be automatically tuned to the word image properties. We also developed two new methods for correcting the slope of the handwritten words. Our experiments show that the proposed method achieves good recognition rates compared to standard classification methods.

Handwritten word recognition using lexicon free and lexicon directed word recognition algorithms

Abstract: The paper discusses the relative merits and complexities of two word recognition algorithms: lexicon directed and lexicon free techniques. This algorithm operates on a pre-segmented word image and yields the optimum concatenation of the image segments for each word in the lexicon. However, the computational complexity of this algorithm is quite high, as the optimum concatenation is required for every word in the lexicon. In the lexicon free word matching process, the character likelihood for all the letters are calculated and the maximum likelihood value and the associated letter are determined. In this approach an optimum string results from the concatenation process. The word matching process is applied only once for an input word image. Comparative results with regard to accuracy, speed and size of lexicon are presented.

Keyword spotting for cursive document retrieval

Abstract: We present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented.

A fast HMM algorithm for on-line handwritten character recognition

Abstract: A fast HMM algorithm is proposed for on-line hand written character recognition. After preprocessing input strokes are discretized so that a discrete HMM can be used. This particular discretization naturally leads to a simple procedure for assigning initial state and state transition probabilities. In the training phase, complete marginalization with respect to state is not performed (constrained Viterbi). A simple smoothing/flooring procedure yields fast and robust learning. A criterion based on the normalized maximum likelihood ratio is given for deciding when to create a new model for the same character in the learning phase, in order to cope with stroke order variations and large shape variations. Preliminary experiments are done on the new Kuchibue database from the Tokyo University of Agriculture and Technology. The results seem to be encouraging.

Machine and human recognition of segmented characters from handwritten words

Abstract: Handwritten character recognition by human readers, a statistical classifier, and a neural network is compared to know the required accuracy for handwritten word recognition. Sample characters extracted from postal address words on mail pieces collected by USPS were used to evaluate human and machine performance. Experimental results show that: 1) when the characters are segmented from words and are randomly presented, the accuracy of the machine recognition is comparable with the average human recognition accuracy, 2) the neural network employing the feature vector of size 64 outperforms the statistical classifier employing the same feature vector, and that 3) the statistical classifier employing the feature vector of size 400 achieves comparable recognition rate with the best human reader.

Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields

Abstract: Check amount recognition is one of the most promising commercial applications of handwriting recognition This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks Special attention is paid to a reliable procedure developed to reject doubtful answers For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself are presented We also present the explanation of problems existing at each stage and their possible solutions The numeral recognizer used to read the amounts written in figures is described This recognizer is based on the procedure of matching input subgraphs to graphs of symbol prototypes Main principles of the handwriting recognizer used to read amounts written in words are explained The recognizer is based on the idea of describing the handwriting with the most stable handwriting elements The concept of the optimal confidence level of the recognition answer is introduced It is shown that the conditional probability of the answer correctness is an optimal confidence level function The algorithms of the optimal confidence level estimation for some special cases are described The sophisticated algorithm of cross validation between legal and courtesy amount recognition results based on the optimal confidence level approach is proposed Experimental results on real checks are presented The recognition rate at 1% error rate is 67% The recognition rate without reject is 85% Significant improvement is achieved due to legal amount processing in spite of a relatively low recognition rate for this field

Method and apparatus for on-line handwriting recognition based on feature vectors that use aggregated observations derived from time-sequential frames

Abstract: A method for on-line handwriting recognition is based on a hidden Markov model and implies the following steps: sensing real-time at least an instantaneous write position of the handwriting, deriving from the handwriting a time-conforming string of segments each associated to a handwriting feature vector, matching the time-conforming string to various example strings from a data base pertaining to the handwriting, and selecting from the example strings a best-matching recognition string through hidden-Markov processing, or rejecting the handwriting as unrecognized. In particular, the feature vectors are based on local observations derived from a single segment, as well as on compacted observations derived from time-sequential segments.

Recovery of temporal information of cursively handwritten words for on-line recognition

Abstract: On-line recognition differs from off-line recognition in that additional information about the drawing order of the strokes is available. This temporal information makes it easier to recognize handwritten texts with an on-line recognition system. In this paper we present a method for the recovery of the stroke order from static handwritten images. The algorithm was tested by classifying the words of an off-line database with a state-of-the-art on-line recognition system. On this database with 150 different words, written by four cooperative writers, a recognition rate of 97.4% was obtained.

Constrained corrective training for continuous parameter system

Abstract: A method is provided for training a statistical pattern recognition decoder on new data while preserving its accuracy of old, previously learned data. Previously learned data are represented as constrained equations that define a constrained domain (T) in a space of statistical parameters (K) of the decoder. Some part of a previously learned data is represented as a feasible point on the constrained domain. A training procedure is reformulated as optimization of objective functions over the constrained domain. Finally, the constrained optimization functions are solved. This training method ensures that previously learned data is preserved during iterative training steps. While an exemplary speech recognition decoder is discussed, the inventive method is also suited to other pattern recognition problems such as, for example, handwriting recognition, image recognition, machine translation, or natural language processing.

High accuracy handwritten Chinese character recognition by improved feature matching method

Abstract: Proposes some strategies to improve the recognition performance of a feature matching method for handwritten Chinese character recognition (HCCR). Favorable modifications are given to all stages throughout the recognition. In pre-processing, we devised a modified nonlinear normalization algorithm and a connectivity-preserving smoothing algorithm. For feature extraction, an efficient directional decomposition algorithm and a systematic approach to design a blurring mask are presented. Finally, a modified LVQ3 algorithm is applied to optimize the reference vectors for classification. The integrated effect of these strategies significantly improves the recognition performance. Recognition results on the large-vocabulary databases ETL8B2 and ETL9B are promising.

Coding and comparison of DAG's as a novel neural structure with applications to on-line handwriting recognition

Abstract: This paper applies directed acyclic graphs (DAGs) to a large class of (temporal) pattern recognition problems and other recognition problems where the data has a linear ordering. The data streams are coded (DAG-coded) into DAGs for robust segmentation. The similarity of two streams can be manifested as the path matching score of the two corresponding DAGs. This paper also presents an efficient and robust dynamic programming algorithm for their comparisons (DAG-compare). Since the DAG-coding methodology directly provides a robust segmentation process, it can be applied recursively to create a novel system architecture. The DAG structure also allows adaptive restructuring, leading to a novel approach to neural information processing. By using these elementary operations on DAGs, we can recognize on average 94.0% (writer-dependent) of the isolated handwritten cursive characters. DAG-coding may also be applied to speech recognition or any other continuous streams where a robust multipath segmentation aids the recognition process.