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This paper presents a candidate lattice refinement method for online handwritten Japanese text recognition. In the integrated segmentation-recognition framework, we first over-segment a character string pattern into primitive segments at least at their true boundaries so that each primitive segment may compose a single character or a part of a character. Then a candidate lattice is constructed based on the primitive segments. We search within the candidate lattice to obtain the optimal path as recognition result. In striving for high recognition accuracy, however, the approach must generate many candidate lattice nodes, which ultimately increase the recognition time. To solve this problem, we refine the candidate lattice to eliminate unnecessary nodes before path search and text recognition. For the refinement, we evaluate all segmentation hypotheses by combining the probability of a character verifier using noncharacter samples, the class-independent unary and binary geometric context, as well as character segmentation. We retain N-best paths by beam search to reduce the complexity of the candidate lattice. Experiments on horizontal text lines extracted from the Kondate database show that the proposed method keeps recognition accuracy while reducing recognition time to half.

This paper describes an online handwritten cursive word recognition approach by combining segmentation-free and segmentation-based methods. To search the optimal segmentation and recognition path as the recognition result, we can attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths are evaluated by integrating character recognition scores with geometric characteristics of the character patterns in a Conditional Random Field (CRF) model. We make a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method, and then attempt combining the two methods to improve performance. Our methods restrict the search paths from the trie lexicon of words and preceding paths during path search. We show this comparison on a publicly available dataset (IAM-OnDB).

This paper describes a comparison between online handwritten cursive word recognition using segmentation-free method and that using segmentation-based method. To search the optimal segmentation and recognition path as the recognition result, we attempt two methods: segmentation-free and segmentation-based, where we expand the search space using a character-synchronous beam search strategy. The probable search paths are evaluated by integrating character recognition scores with geometric characteristics of the character patterns in a Conditional Random Field (CRF) model. Our methods restrict the search paths from the trie lexicon of words and preceding paths during path search. We show this comparison on a publicly available dataset (lAM-OnDB).

The paper presents a recognition method of character-position-free (CPF) on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we prepared large sets of CPF handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by the Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, and verify that it produces competing recognition rates as the latest recognizer for normally handwritten horizontal Japanese text without the serious problem in speed for practical applications.

This paper describes significant improvement in On-line handwritten Japanese text recognition that is free from line direction and character orientation constraints. The original system [1, 2] separates freely written text into text line elements, estimates and normalizes character orientation and line direction. Then, it hypothetically segments each text line element into primitive segments, constructs a segmentation-recognition candidate lattice and evaluates the likelihood of candidate segmentation-recognition paths by combining the scores of character recognition, geometric features, as well as linguistic context. In this scheme, we have updated the over-segmentation for each text line element and applied a robust context integration model to recognize each text line element. Experimental results on text from the HANDS-Kondate t bf-2001-11 database demonstrate large improvement in the character recognition rate compared with the previous system [1, 2].

This paper presents a semi-incremental recognition method for online handwritten English text. 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 word 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 IAM-OnDB database shows the effectiveness of the proposed method not only in reduced processing time and waiting time, but also in recognition accuracy.

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.

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 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.

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 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.

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.

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.

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 presents a revised method for keyword search from Japanese handwritten digital ink. We employ Japanese string recognition and produce a candidate lattice. We search for a given keyword into the lattice so that we can search for the keyword even if constituent characters are not in the top candidates. We present some overall performance as well as consideration on search errors.

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 system’s overall accuracy (f-measure) from 0.653 to 0.891.

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 discusses online handwriting recognition of Japanese characters, a mixture of ideographic characters (Kanji) of Chinese origin, and the phonetic characters made from them. Most Kanji character patterns are composed of multiple subpatterns, called radicals, which are shared among many (sometimes hundreds of) Kanji character patterns. This is common in Oriental languages of Chinese origin, i.e., Chinese, Korean and Japanese. It is also common that each language has thousands of characters. Given these characteristics, structured character pattern representation (SCPR) composed of subpatterns is effective in terms of the size reduction of a prototype dictionary (a set of prototype patterns) and the robustness to deformation of common subpatterns. In this paper, we show a prototype learning algorithm and HMM-based recognition for SCPR. Then, we combine the SCPR-based online recognizer with a compact offline recognizer employing quadratic discriminant functions. Moreover, we also discuss online handwritten Japanese text recognition and propose character orientation-free and line direction-free handwritten text recognition and segmentation. Finally, as applications of online handwritten Japanese text recognition, we show segmentation of mixed objects of text, formulas, tables and line-drawings, and handwritten text search.

This paper describes prototyping of a form 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 form dictates how to process the form so that wecall the form "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.