Masaki Nakagawa

Bio: Masaki Nakagawa is an academic researcher from Tokyo University of Agriculture and Technology. The author has contributed to research in topics: Handwriting recognition & Intelligent word recognition. The author has an hindex of 26, co-authored 268 publications receiving 2900 citations. Previous affiliations of Masaki Nakagawa include University at Buffalo & University of Tokyo.

'Online recognition of Chinese characters: the state-of-the-art

Abstract: Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

Collection of on-line handwritten Japanese character pattern databases and their analyses

Abstract: We describe the design of on-line handwritten Japanese character pattern databases, software tools for pattern collection and verification, and analyses of collected patterns. Two databases containing over 3 million patterns were compiled: one with 120 people contributing 12,000 patterns each and another with 163 participants contributing 10,000 patterns each. Patterns were collected mostly in their sentential contexts and verified by machine and human inspection. Their analyses reveal greater variations in stroke count for characters having many strokes, with people generally using fewer strokes; they additionally reveal that stroke order variations are generally caused by common habits and added strokes.

Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields

Abstract: This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition

Abstract: This paper compares the current state of the art in online Japanese character recognition with techniques in western handwriting recognition. It discusses important developments in preprocessing, classification, and postprocessing for Japanese character recognition in recent years and relates them to the developments in western handwriting recognition. Comparing eastern and western handwriting recognition techniques allows learning from very different approaches and understanding the underlying common foundations of handwriting recognition. This is very important when it comes to developing compact modules for integrated systems supporting many writing systems capable of recognizing multilanguage documents.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Touch screen device, method, and graphical user interface for determining commands by applying heuristics

Abstract: A computer-implemented method for use in conjunction with a computing device with a touch screen display comprises: detecting one or more finger contacts with the touch screen display, applying one or more heuristics to the one or more finger contacts to determine a command for the device, and processing the command. The one or more heuristics comprise: a heuristic for determining that the one or more finger contacts correspond to a one- dimensional vertical screen scrolling command, a heuristic for determining that the one or more finger contacts correspond to a two-dimensional screen translation command, and a heuristic for determining that the one or more finger contacts correspond to a command to transition from displaying a respective item in a set of items to displaying a next item in the set of items.

Portable electronic device with interface reconfiguration mode

Abstract: A portable electronic device displays a plurality of icons (e.g., graphical objects) in a region in a touch-sensitive display; detects a predefined user action, with respect to the touch-sensitive display, for initiating a predefined user interface reconfiguration process; and varies positions of one or more icons in the plurality of icons in response to detecting the predefined user action. The varying includes varying the positions of the one or more icons about respective average positions.