A formalization of on-line handwritten Japanese text recognition free from line direction constraint

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

2005

Self Cite

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_t_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.

Section: Introductionmentioning

confidence: 99%

Segmentation of on-line handwritten Japanese text of arbitrary line direction by a neural network for improving text recognition

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

2005

Self Cite

“…This paper will describe the details of the step 1. For the step 2 and step 3, refer to the literature [4], [7]. …”

Section: Step3: Segmentation and Recognitionmentioning

confidence: 99%

“…We incorporate the method into the segmentation by recognition scheme. We follow the stochastic model proposed in [7] to evaluate the likelihood composed of character pattern structure, character segmentation, character recognition and context and finally determine segmentation points and recognize text.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of On-Line Handwritten Japanese Text Using SVM for Improving Text Recognition

Tokuno

Document Analysis Systems VII

2006

Abstract. This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional features from on-line strokes of handwritten text and applies the SVM to the extracted features to produces segmentation point candidates. We incorporate the method into the segmentation by recognition scheme based on a stochastic model which evaluates the likelihood composed of character pattern structure, character segmentation, character recognition and context to finally determine segmentation points and recognize handwritten Japanese text. This paper also shows the details of generating segmentation point candidates in order to achieve high discrimination rate by finding the combination of the segmentation threshold and the concatenation threshold. We compare the method for segmentation by the SVM with that by a neural network using the database HANDS- Kondate_t_bf-2001-11 and show the result that the method by the SVM bring about a better segmentation rate and character recognition rate.

“…We made a model and recently formalized it for online handwritten Japanese text recognition free from line-direction constraint and writing format constraint, such as character writing boxes or ruled lines [42] [52]. The model evaluates the probability of character segmentation, character recognition, character pattern structure, and context.…”

Section: Model Of Free Format Recognitionmentioning

confidence: 99%

Recent Results of Online Japanese Handwriting Recognition and Its Applications

Tokuno

Arabic and Chinese Handwriting Recognition

et al.

Abstract. 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.