Collecting handwritten mathematical expressions (HMEs) generally requires a lot of time and effort for data preparation, data collection, annotation and so on. In this paper, we present a method for generating realistic HMEs in a wide variety of structures and styles from a LaTeX sequence or a MathML script using online isolated symbol patterns. Our method firstly positions all symbols in a symbol relation tree constructed from the input LaTeX or MathML script. Then, it places normalized online symbol patterns into the corresponding locations. A questionnaire-based experiment shows that the synthetic patterns are as clear and natural as the real patterns. Therefore, we can use the generated synthetic HME patterns for research on HME recognition and clustering.

This paper presents an attention-based convolutional sequence to sequence (ACseq2seq) model for recognizing an input image of multiple text lines from Japanese historical documents without explicit segmentation of lines. The recognition system has three main parts: a feature extractor using Convolutional Neural Network (CNN) to extract a feature sequence from an input image; an encoder employing bidirectional Long Short-Term Memory (BLSTM) to encode the feature sequence; and a decoder using a unidirectional LSTM with the attention mechanism to generate the final target text based on the attended pertinent features. We also introduce a residual LSTM network between the attention vector and softmax layer in the decoder. The system can be trained end-to-end by a standard cross-entropy loss function. In the experiment, we evaluate the performance of the ACseq2seq model on the anomalously deformed Kana datasets in the PRMU contest. The results of the experiments show that our proposed model achieves higher recognition accuracy than the state-of-the-art recognition methods on the anomalously deformed Kana datasets.

Despite of recent breakthroughs in the accuracy of single character recognition using the deeper convolution neural networks, one of the remaining problems is that OCRs almost fail to recognize character patterns when they are severely degraded, especially those of the historical documents. Another problem to recognize characters in historical documents is the lack of sufficient training patterns because of the heavy cost for annotation. This paper proposes a character attention generative adversarial network named CAGAN for restoring heavily degraded character patterns in historical documents so that OCRs improve their accuracy and even help archeologists to decode them. The network is based on the U-Net like architecture [1] with skip connections, and it is trained by the proposed loss function including the common adversarial loss (global loss) and the hierarchical character attentive loss (local loss). We made an experiment on 118 categories of most common Japanese Kanji characters, collected from severely damaged historical documents called Heijokyo mokkan written during the Nara period in Japan. The experiment shows that our method restores the shapes of characters and improves the recognition rate significantly, which is helpful for archeologists to decode damaged character patterns.

This paper proposes a method for speeding upparsing process for recognizing online handwritten mathematicalexpressions (OHME). We prune infeasible partitions in theparsing table to reduce the time for the parsing process. Lowscore partitions are candidates for pruning. Our method can beapplied for any parsing algorithms that use score functions. Inthis paper, we use a stroke order free system as a baseline system.The method is as follows. First, we analyze the scores ofpartitions in each row of the parsing table. Then, we determine athreshold for each row to prune low score partitions. Finally, weemploy these thresholds to prune low score partitions on thebaseline recognition system. The results of evaluations of ourmethod on the CROHME 2014 database show that therecognition process is speeded up by 3.46 times and 4.97 timeswhile recognition rate is reduced only 0.31 point and 0.71 point,respectively.

Motivated by recent successes in neural machine translation and image caption generation, we present an end-to-end system to recognize Online Handwritten Mathematical Expressions (OHMEs). Our system has three parts: a convolution neural network for feature extraction, a bidirectional LSTM for encoding extracted features, and an LSTM and an attention model for generating target LaTex. For recognizing complex structures, our system needs large data for training. We propose local and global distortion models for generating OHMEs from the CROHME database. We evaluate the end-to-end system on the CROHME database and the generated databases. The experiential results show that the end-to-end system achieves 28.09% and 35.19% recognition rates on CROHME without and with the generated data, respectively.

This paper proposes a modified X-Y cut method for reordering strokes of online handwritten mathematical expression (ME) in order to make stroke order free recognition. To deal with overlapping, which causes problems in the X-Y cut method, we determine vertically ordered strokes by detecting vertical symbols and its upper/lower MEs. An upper ME and a lower ME are treated as MEs which are reordered recursively. Unordered strokes on the left side of a vertical symbol are reordered as horizontally ordered strokes. The remaining strokes are reordered recursively. The horizontally ordered strokes are reordered from left to right and the vertically ordered strokes are reordered from top to bottom. The results of evaluations of the reordering method on the CROHME 2014 database show that our ME recognition system incorporating this method outperforms all other systems that use only CROHME 2014 for training while the processing time is kept to a practical level.

This paper presents a system for recognizing online handwritten mathematical expressions (MEs) and improvement of structure analysis. We represent MEs in Context Free Grammars (CFGs) and employ the Cocke-Younger-Kasami (CYK) algorithm to parse 2D structure of on-line handwritten MEs and select the best interpretation in terms of symbol segmentation, recognition and structure analysis. We propose a method to learn structural relations from training patterns without any heuristic decisions by using two SVM models. We employ stroke order to reduce the complexity of the parsing algorithm. Moreover, we revise structure analysis. Even though CFG does not resolve ambiguities in some cases, our method still gives users a list of candidates that contain expecting result. We evaluate our method in the CROHME 2013 database and demonstrate the improvement of our system in recognition rate as well as processing time.

This paper presents a semi-incremental recognition method for online Japanese handwritten text recognition, which is used for busy recognition interface (recognition while writing) and lazy recognition interface (recognition after writing) without large waiting time. We employ local processing strategy and focus on a recent sequence of strokes defined as "scope". For the latest scope, we build and update a segmentation and recognition candidate lattice and advance the best-path search incrementally. We utilize the result of the best-path search in the previous scope to exclude unnecessary segmentation candidates. This reduces the number of candidate character recognition with the result of reduced processing time. We also reuse the segmentation and recognition candidate lattice in the previous scope for the latest scope. Moreover, triggering recognition processes every few strokes save CPU time. Experiment made on TUAT-Kondate database shows the effectiveness of the proposed method not only in reduced processing time and waiting time, but also in recognition accuracy.

We propose a method which performs an isotropic morphological dilation via implicit smoothing for the purpose of restoring the degraded character shapes of binarized images. Exploiting the idea of geodesic morphology that the binary image and its distance transformed image are interconvertible, we apply a smoothing method not to the binary image but to the distance transformed image, and then reconvert it by binarization. This allows us to apply conventional smoothing methods for continuous intensity, i.e., gray scale, images to the discrete intensity, i.e., binary, image implicitly. For instance, by using an isotropic diffusion together with geodesic dilation, an isotropic dilation along the stroke direction is obtained and brings better results.

We propose a segmentation based online word recognition approach which uses a Conditional Random Field (CRF) driven beam search strategy. An efficient trie-lexicon directed, breadth-first beam search algorithm is employed in a combined segmentation-and-recognition framework to accomplish real-time recognition of online handwritten cursive English words. This framework is developed by building a candidate lattice of primitive segments obtained through over segmentation of the word pattern. The search space for the lattice is expanded by synchronously matching the lattice nodes to likely character patterns from a trie-dictionary constructed out of the target lexicon. The probable paths are evaluated by integrating character recognition scores with physical and spatial characteristics of the handwritten segments in a CRF (conditional random field) model and a beam search strategy is used to prune the set of likely paths. This approach has been benchmarked on the new IBM_UB_1 dataset as well as on the UNIPEN dataset for comparison.

This paper describes an online handwritten English cursive word recognition method using a segmentation-free Markov random field (MRF) model in combination with an offline recognition method which uses pseudo 2D bi-moment normalization (P2DBMN) and modified quadratic discriminant function (MQDF). It extracts feature points along the pen-tip trace from pen-down to pen-up and uses the feature point coordinates as unary features and the differences in coordinates between the neighboring feature points as binary features. Each character is modeled as a MRF and word MRFs are constructed by concatenating character MRFs according to a trie lexicon of words during recognition. Our method expands the search space using a character-synchronous beam search strategy to search the segmentation and recognition paths. This method restricts the search paths from the trie lexicon of words and preceding paths, as well as the lengths of feature points during path search. We also combine it with a P2DBMN-MQDF recognizer that is widely used for Chinese and Japanese character recognition.

We have many historical documents written in over 1,000 years ago. Shape features of character patterns on the documents are unstable or missing because most of the documents have been stained and degraded deeply. Digital archives of the documents with accurate character pattern retrieval methods are helpful for archaeologists and historians. In this paper, we propose a similarity evaluation method for character patterns with missing shape parts. It collaboratively works with non-linear normalization for such patterns, and modifies the templates for each trial of the retrieval efficiently. In the experiences using 4,911 Kanji (Chinese origin) character patterns from the Japanese historical documents called mokkans, the method shows improvements of the retrieval accuracy. Also, we present a simple implementation of gradient feature extraction to compare the chain code feature with the gradient feature in the retrieval. As the result, the gradient feature works better than the chain code feature.

This paper presents the effect of text/non-text classification for ink search which employs string recognition. Pen or touch interfaces provides the benefit that users can write text and draw figures without changing the device or mode, but line drawings are troublesome for ink search. We propose the insertion of text/non-text classification before ink search and show its effect. For ink search, we employ our own engine to search keywords in the candidate lattice prepared by on-line handwritten Japanese text recognition, since this method produces higher search rate for Japanese text in digital ink than word spotting without ink recognition.

In this paper, we present methods of segmenting Nom historical documents and clustering character patterns to build a Nom character pattern database. Nom is an ideographic script to represent Vietnamese, used from the 10th century to 20th century. However, this heritage is nearly lost. In order to preserve the wisdom and knowledge expressed in Nom, recognition and digitalization are indispensable. Because there is no OCR for Nom yet, we have to start from collecting patterns. We have employed a projection profile based method for segmenting hundreds of pages into individual characters. Then, we have implemented a combination of Chinese OCR-based clustering and K-means clustering to group characters into categories. The experiment shows that the proposed system can help collecting the characters patterns effectively. Moreover, it has revealed that there are many character classes lost or uncategorized so far.

This paper describes effects of a large amount of artificial patterns to train an on-line handwritten Japanese character recognizer. In general, as more learning patterns employed for training pattern recognition systems, as higher recognition rate is obtained. In reality, however, the existing pattern samples are not enough, especially for languages of a large character set. Therefore, for on-line handwritten Japanese character recognition, we construct six linear distortion models and combine them with a nonlinear distortion model to generate a large amount of artificial patterns. We apply the method for the TUAT Nakayosi database and train a recognizer while evaluate the effects for the TUAT Kuchibue database with the remarkable effects of improving recognition accuracy.

This paper describes an unconstrained on-line handwritten Japanese text retrieval system from character recognition candidates. The system is based on a discriminative model which integrates the scores of character recognition, segmentation and geometric context in search and retrieval, and the parameters are trained by supervised learning. Experiments on TUAT Kuchibue database show that the proposed method can effectively improve the system performance. When the search method with the optimal threshold retrieves for a keyword consisting of two, three or four characters, its f-measure is 0.720, 0.868 or 0.923, respectively.

In this paper, we describe a method of retrieving on-line handwritten text based on directional feature matching. Although text search into the character recognition candidate lattice has been elaborated, the character recognition based approach does not support languages which are not assumed. The proposed method is liberated from this constraint. It first hypothetically segments on-line handwritten text into character pattern blocks and prepares the object text patterns by combining the character pattern blocks. On the other hand, it employs handwritten text as a query pattern or prepares a query pattern by combining character ink patterns from query character codes. Then, it extracts directional features from the object text patterns and the query pattern, and the dimensionalities of those features are further reduced by Fisher linear discriminate analysis (FDA). Finally, the similarity is measured between the object text patterns and the query pattern by block-shift matching. This paper discusses the retrieval performance in comparison with our previous character recognition based method.

In offline handwritten character recognition, the nonlinear normalization (NLN) method based on line density equalization has been proven very effective. This paper shows the effects on online handwritten Japanese character recognition. We apply the nonlinear normalization based on line density equalization to online character patterns. Since the curve-fitting-based normalization methods and their pseudo 2D extensions yields superior performance on offline patterns, we also combine these methods with the way using line density projection. We have compared the methods using trajectory-based projection with ones using line density projection. As a result, line density-based methods yield superior accuracy and a competitive time-complexity.

This paper describes the design and implementation of a recognition server for online handwritten Japanese text. Currently, fast and accurate recognition of online handwritten characters requires a high-performance CPU and a large memory space. However, PDA and other small portable devices lack the qualification. On the other hand, data transmission speed is getting remarkably higher. To solve the CPU and memory constraint on a client and avail the speed-up of transmission, we employ a network environment and build an a recognition server. This paper descrives network data flow transmission, client/server architecture and presents performance.

This paper presents a conditional random field (CRF) model for aligning online handwritten Chinese/Japanese text lines (character strings) with the corresponding transcripts. The CRF model is defined on a lattice which contains all possible segmentation hypotheses. The feature functions characterize the shape and context dependences of characters, including the scores of character recognition and the geometric compatibilities between characters. The combining parameters are optimized by energy minimization. Experimental results on two online databases: CASIA-OLHWDB and TUAT Kondate demonstrate the effectiveness of the proposed method.

This paper describes effective object function design for combining on-line and off-line character recognizers for on-line handwritten Japanese text recognition. We combine on-line and off-line recognizers using a linear or nonlinear function with weighting parameters optimized by the MCE criterion. We apply a k-means method to cluster the parameters of all character categories into groups so that the categories belonging to the same group have the same weight parameters. Moreover, we apply a genetic algorithm to estimate super parameters such as the number of clusters, initial learning rate and maximum learning times as well as the sigmoid function parameter for MCE optimization. Experimental results on horizontal text lines extracted from the TUAT Kondate database demonstrate the superiority of our method.

This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese characters. It also presents updated evaluation using a large testing set. The model extracts feature points along the pen-tip trace from pen-down to pen-up and sets each feature point from an input pattern as a site and each state from a character class as a label. It employs the coordinates of feature points as unary features and the differences in coordinates between the neighboring feature points as binary features. The weighting parameters are estimated by CRF or the minimum classification error (MCE) method. In experiments using the TUAT Kuchibue database, the method achieved a character recognition rate of 92.77%, which is higher than the previous model's rate, and the method of estimating the weighting parameters using CRF was more accurate than using MCE.

This paper describes a method for constructing the most efficient and robust coarse classifier from a large number of basic recognizers which are obtained by different parameters of feature extraction, different discriminant methods or functions, and so on. The architecture of the coarse classification is a sequential cascade of basic recognizers and reduces the candidates after each basic recognizer. Genetic algorithm determines the best cascade with the best speed and highest performance. The method is applied for on-line handwritten Japanese characters recognition. We produced 201 basic recognizers of MQDF, 21 basic recognizers of Euclidian distance and 21 basic recognizers of the LSS method by changing parameters. From these basic recognizers we have obtained a rather simple 2 stages cascade with the result that the whole recognition time was reduced to 24.5% while keeping classification and recognition rates.

This paper describes a lexicon-driven approach to on-line handwritten Japanese disease name recognition using a time-synchronous method. A trie lexicon is constructed from a disease name database containing 21,713 disease name phrases. It expands the search space using a time-synchronous method and applies the beam search strategy to search into a segmentation candidate lattice constructed based on primitive segments. This method restricts the character categories for recognizing each character candidate pattern from the trie lexicon of disease names and preceding paths during path search in the segmentation candidate lattice, and selects an optimal disease name from the disease name database as recognition result. The experimental results demonstrate the effectiveness of our proposed method, which improves character recognition rate from 94.56% to 99.97% compared with a general-purpose Japanese text recognizer and speeds up recognition time as 4.3 times faster as the general recognizer.

This paper presents a revised method for keyword search from handwritten digital ink in comparison with the previous system. We adopt a search method using noise reduction. Experiments on digital ink databases show that the revised method typically improves the systempsilas overall accuracy (f-measure) from 0.653 to 0.891.

This paper describes an online handwritten Japanese character string recognition system based on conditional random fields, which integrates the information of character recognition, linguistic context and geometric context in a principled framework, and can effectively overcome the variable length of candidate segmentation. For geometric context, we employ both unary and binary feature functions, as well as the ones relevant and irrelevant to character classes. Experimental results show that the CRF based method outperforms the method with normalized path evaluation criterion, and the geometric context benefits the performance significantly.

This paper describes a method of on-line handwritten Japanese text recognition by improved path evaluation. Based on a theoretical ground, the method evaluates the likelihood of candidate segmentation paths by combining scores of character pattern size, inner gap, character recognition, single and pair character position, candidate segmentation point and linguistic context, with the weight parameters optimized by a genetic algorithm. The path score is insensitive to the number of candidate patterns and the optimal path can be found by Viterbi search. Experimental results demonstrate the superiority of the proposed method.

This paper presents a design and an implementation of document image retrieval to support reading mokkans. A mokkan is a wooden tablet with text written by a brush in India ink. Despite the archaeological and historical value of the mokkans excavated from ancient ruins, many of the mokkans have not been decoded yet due to the lost or too much damaged character patterns on them. Character recognition for damaged patterns is useful to decode such mokkans. Furthermore, if the recognition results show not only the character codes but also the images of the character patterns and the whole mokkans, the recognition becomes useful document retrieval to complement the lost or unreadable part of the mokkans. In the implementation, we built a public database of historical mokkans with their photographs and a character recognition module working on our support system to search the database. The evaluation by archaeologists is in progress.

This paper presents a visualization tool to improve the performance of a classifier based on the hidden Markov Model. A specific recognition system for which the visual- ization tool is designed is an on-line handwritten Japanese character recognition system. The recognition system was built from already estimated parameter values, which leads to some difficulties when trying to adjust the system. To tackle this problem we describe how visual information can be helpful to interpret the results and how it can be used to build a set of viewers for helping the tuning task. These viewers were used to examine the data structure and inter- nal procedures of the recognition engine allowing to detect and correct errors in the first implementation. We conclude the paper comparing the two implemented versions of the classifier by showing the increase we achieved in recogni- tion accuracy.

This paper describes a support system for archeologists to read "mokkan". A mokkan is a wooden tablet on which text was written by a brush. Many mokkans used in Nara period (from AD. 710 to 794) are being excavated from Heijyo-kyo, Japan (the ancient court in the Nara period). The support system is for archeologists who read mokkans that have been stained, damaged and degraded under the soil. Such mokkans are hard to read even for expert readers. However, the binarization functions of the system extract ink from the image of the mokkans and the character recognition function outputs candidates even for degraded or partially missing character patterns. We made also a graphical user interface to invoke the above functions, provide experts with suggestions and stimulate their inference. Archeologists in the experiment for evaluation enthusiastically accepted the system.

This paper describes a segmentation method of online handwritten Japanese text of arbitrary line direction by a neural network to improve text recognition performance. This method extracts multidimensional features from strokes of handwritten text and input them into a neural network to preliminarily determine segmentation points. Then, it modifies segmentation candidates using some spatial features. We compare the method with the previous method and that by Fisher's linear discriminant using the database HANDS-Kondate/spl I.bar/t/spl I.bar/bf-2001-11. This paper also shows how to generate character segmentation candidates in order to achieve high discrimination rate by investigating the relationship between recall, precision and the f measure.

We describe here information encoding and decoding methods applied to dot texture for active forms. We employ dot texture made of tiny dots and looking like gray color to print various forms. This facilitates the separation of handwriting from its input frame even under monochrome printing/reading environments. It also makes the forms determine how to process filled-in handwriting according to the information embedded in the dot texture. The embedded information results in an improved recognition rate of handwriting, and allows the form processing to be directed by the form itself rather than by the form reading machine. Thus, the form-reading machine becomes a general-purpose machine allowing different forms inputted into it to be processed differently as specified by each form. We compare various dot shapes and information encoding/decoding methods for those shapes. Then, we present how to locate input frames, separate handwriting from input frames and segment handwriting into characters. We also present preliminary evaluation of the described methods.

Despite all the attempts of modern information technologyto supplant paper by sophisticated electronic devices,paper is still omnipresent in virtually any office environment.We have learned over the last decade that paper isan indispensable part of our daily work and that even thelatest computers are not able to mimic its functionality. Today,paper and computer are coexisting side by side withoutinteracting with each other, both having their specificadvantages. This paper presents a concept for bridging thegap between computer and paper by combining the advantagesof both electronic and paper world. The implementationdescribed in this paper augments standard paper withfeatures of modern information technology. Moreover, it allowsinterfacing computers using pen and paper.

This paper describes an on-line handwritten Japanese text recognition method that is liberated from constraints on writing direction (line direction) and character orientation. This method estimates the line direction and character orientation using the time sequence information of pen-tip coordinates and employs writing-box-free recognition with context processing combined. The method can cope with a mixture of vertical, horizontal and skewed lines with arbitrary character orientations. It is expected useful for tablet PCs, interactive electronic whiteboards and so on.

This paper describes prototyping of a processingsystem employing dot texture for printing input frames ofthe form. The dot texture is the texture composed of smalldots. It eases the separation of handwritings from theinput frames even under monochrome printing/readingenvironments and makes the system to process thehandwritings according to the information embedded inthe dot texture of the frames. The embedded informationin the dictates how to process the so that wecall the active form being opposite to the passiveform processed by the program stored in a documentreader. This method can also be used to embed otherinformation such as attribute of handwriting and so on.This paper presents the design, prototyping and somepreliminary evaluation.

This paper proposes a new approach to accelerating speed and increasing the recognition rate of an off-line recognizer employed for on-line handwriting recognition of Japanese characters. All training patterns are divided according their stroke number to several groups and one single recognizer is dedicated for each group of patterns. Since a number of categories for a single recognizer is smaller, the speed and accuracy improves. First, we make the model of a recognizer and show that our method can theoretically accelerate its recognition speed to 45 % of the original time. Then, we employ the method to a practically used off-line recognizer with the result that the recognition rate is increased from 90.73 % to 91.60 % and the recognition time is reduced to only 49.73 % of the original one. Another benefit of our new approach is high scalability so that the recognizer can be optimized for speed and size or for the best accuracy. 1.

This paper proposes a method to accelerate character recognition of a large character set by employing pivots into the search space. We divide the feature space of character categories into smaller clusters and derive the centroid of each cluster as a pivot. Given an input pattern, it is compared with all the pivots and only a limited number of clusters whose pivots have higher similarities (or smaller distances) to the input pattern are searched for with the result that we can accelerate the recognition speed. This is based on the assumption that the search space is a distance space. The method has been applied to pre-classification of a practical off-line Japanese character recognizer with the result that the pre-classification time is reduced to 61 % while keeping its pre-classification recognition rate up to 40 candidates as the same as the original 99.6% and the total recognition time is reduced to 70% of the original time without sacrificing the recognition rate at all. If we sacrifice the pre-classification rate from 99.6% to 97.7%, then its time is reduced to 35% and the total recognition time is reduced to 51.5% with recognition rate as 96.3% from 98.3%.

This paper presents the UP-Kuchibue and UP-Nakayosi databases containing on-line handwritten Japanese characters. These databases are the international versions of two databases, Kuchibue and Nakayosi, collected in the Nakagawa Laboratory at the University of Agriculture & Technology in Tokyo. They contain more than 3 million characters written by 283 Japanese writers. UP-Kuchibue and UP-Nakayosi are stored in the common Unipen format. Unipen is a western plain ASCII format which allows easy access for international researchers and facilitates international benchmarks.

This paper describes our second collection of online handwritten character patterns and their analysis. 163 writers presented about 10,000 character patterns, covering 4,438 categories mainly in the context of sentences. Together with our first collection, the Kuchibue database containing 12,000 patterns from 120 writers, we have now collected about 3 million patterns. For this second collection of online patterns, named Nakayosi, we analyzed stroke number and order variations.

The availability of a large sample database is very important to design high accuracy classifiers for handwritten character recognition. Collecting image samples from human writers and practical documents is expensive particularly for large character sets, like with East-Asia-languages. We can therefore take advantage of existing online databases to generate additional off-line images. This paper proposes a method to generate realistic character images from online patterns. From the pen trajectory of an online pattern, the proposed method can generate numerous images of various stroke shapes using three painting modes: constant line mode, proportional mode and calligraphic mode. Particularly, the calligraphic mode combines the pen trajectory (representing the writing style of one concrete writer) with real stroke images (also representing individual writing style of a concrete writer) to generate character images that look as if they were produced with a brush or pen by human hand.

Artificial neural networks have been widely used in handwritten numeral recognition (HNR) with some success. This paper presents some new results of HNR using MLP (multilayer perceptron) and RBF (radial basis function) neural networks. By using discriminative training, which aims to minimize the empirical classification error on a training data set, the recognition accuracy is considerably improved. The performance of the RBF net is comparable to that of the MLP in terms of the forced recognition rate, and even better than the MLP in terms of the rejection-error tradeoff. The experiments were implemented on the CENPARMI database, and very high recognition rates have been obtained.

This paper reviews some prototype learning algorithms for nearest neighbor (NN) classifier design land evaluates their performances in handwritten character recognition. The algorithms include the well-known LVQ and those that globally optimize an objective function, as well as some newly derived variants. Experimental results of handwritten numeral recognition and Chinese character recognition show that the global optimization algorithms generally outperform LVQ. Particularly, the generalized LVQ of Sato and Yamada (1998) and a new algorithm MAXP2 yield best results.

The paper describes a relaxation based matching method for offline character recognition. This method employs elastic stroke models as standard character patterns. Pattern similarity between a standard and an input pattern is defined by fuzzy logic. The matching process is formalized as a maximization problem of the similarity and computed by the steepest descent technique. To implement this technique, we adopted automatic differentiation, which made it possible to calculate the partial derivatives of the target function automatically, only given the definition of that function. Results of computer experiments targeting 46 hiragana characters from the ETL8B database revealed a maximum recognition rate of 98.8% for 20 input sets when combining stroke springs with relative location springs.

This paper proposes a model-dependent pattern normalization method that employs an elastic membrane model to absorb pattern deformation in handwritten character patterns. This method prepares an elastic membrane from an input pattern and induces model-dependent pattern normalization by placing the membrane on each potential field representing a standard pattern. We composed a recognition system from non-linear normalization, coarse classification, model-dependent normalization and fine classification, then show that the system with the model-dependent normalization is more effective than that without it for both the ETL off-line handwritten character patterns database and the HANDS on-line patterns database.

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).

The paper presents an offline handwritten character recognition method based on a dynamic system. The method employs rubber string models for standard character patterns (called an "elastic stroke model") and matches them with the potential field made from an input pattern. The matching process is realized by a dynamic relaxation with DP algorithm which minimizes the dynamic energy of rubber and potential field. To study the effect of the local minimum problem inherent in the relaxation technique, we used an improved DP method implemented on a parallel machine to compute the global minimum. Some experiments are performed on the 10 sets of 46 categories from the Japanese set of Hiragana characters that come from the ETL8b2c database. As a result, it is shown that the local minimum has little influence on recognition rates in this method, and the elastic stroke model could absorb deformation of character patterns.

The paper presents a database of on-line handwritten character patterns sampled in a sequence of sentences without any instructions. The sentences according to which character patterns are collected have been picked up from newspaper to include 1227 frequently appearing character categories with the result that they are composed of about 10000 characters and include 1537 JIS 1st level character categories. The rest of the JIS 1st level 1808 categories have been added at the end of the above text and written one by one. The total text has been commonly employed for collecting script patterns from a number of people. Patterns offered were inspected and omissions and wrong patterns were rewritten. The authors collected data from 80 people and made the 12000/spl times/80 patterns available from February 1996. More patterns are being collected. The paper describes the characteristics of this database as well as several tools to collect patterns.

A simple and fast segmentation of text and line drawings which exploits the characteristics of online handwritten patterns is presented. Many attempts have been made on text/figure segmentation in offline research, but none in online. In online environment, mode selection is easily employed to input characters and drawings commongly. But it disturbs the user's thought. The authors propose online but delayed processing of handwritten patterns after they are written. Because the input patterns consists of characters and line drawings, there is a need to discriminate input patterns into characters and line drawings. An analysis of 17 handwritten patterns shows that the logarithmic distribution of character stroke length and that of the length of the longer side of rectangles circumscribing character strokes have similarity to the normal distribution. This finding, the contact frequency of character strokes and drawing strokes, and stroke input order have been used to perform the segmentation. In the testing of 18 patterns, 88% of all strokes were correctly judged.>

Basic design concepts are presented for a creative writing environment with pen and display integrated tablet to promote human thinking rather than to bother a user with the chores of input. The pen is suitable for creative work since one can express almost everything and is not bothered by the method of use. Experimental pen-based systems and products have not exploited the automated nature of handwriting. They try to recognized handwriting immediately after each pattern is written with the result of frequent misrecognition, and thus they interrupt the user's thinking. A structure of representation to handle the ambiguity of handwritten patterns is presented, as well as a lazy recognition scheme which delays the display of recognition until needed. Also presented is a prototype for text preparation, automatic segmentation of text and diagrams and mathematical formula recognition. They are all consolidated with lazy recognition.>

The authors present a handwritten character recognition method along with experiments that were conducted on a system developed to test this method's feasibility. This method can be applied to both online and offline input. Under this method the matching of characters is based on a dynamic model and performed using a relaxation method. A potential field is created to represent the dictionary character. Into this are dropped feature points taken from the input character and matching takes place. Matching thus occurs with the input character distorting to match the dictionary character. Springs are used to control this distortion. Details of variations to this method are given, and the results of comparative experiments conducted on these variations are presented.>