Yojiro Tonouchi

An on-line Japanese character recognition method using length-based stroke correspondence algorithm

Because most Japanese characters consist of several strokes, many recognition methods determine stroke correspondence before calculating the distance between the input character and the template character. When the writing style is cursive, however, the stroke number is changeable. If stroke numbers differ between the input and template characters, stroke correspondence is not one-to-one, and it is not easy to determine the correspondence. The method proposed, uses stroke length, because these lengths are relatively stable even when the writing is cursive. The calculation of recognition using stroke lengths is faster than that of recognition using other multidimensional feature values such as the coordinates. Experimental results show the effectiveness of the proposed method.

Text Input System Using Online Overlapped Handwriting Recognition for Mobile Devices

This paper proposes a novel online overlapped handwriting recognition system for mobile devices such as cellular phones. Users can input characters continuously without pauses on the single writing area. It has three features: small writing area, quick response and direct operations with handwritten gestures. Therefore, it is suitable for mobile devices such as cellular phones. The system realizes a new handwriting interface similar to touch-typing. We evaluated the system by two experiments: character recognition performance and text entry speed of Japanese sentences. Through these experiments we showed the effectiveness of the proposed system.

Path Evaluation and Character Classifier Training on Integrated Segmentation and Recognition of Online Handwritten Japanese Character String

This paper describes a method of online handwritten Japanese character string recognition by improved path evaluation and character classifier training. The path evaluation is insensitive to the segmentation length and the optimal path can be found by dynamic programming (DP). The character classifier training improves resistance to non-character patterns, which is a problem on integrated segmentation and recognition of handwritten character strings. Experimental results show that the path evaluation and the character classifier training improve the performance of string recognition.

A Hybrid Approach to Detect Texts in Natural Scenes by Integration of a Connected-Component Method and a Sliding-Window Method

Text detection in images of natural scenes is important for scene understanding, content-based image analysis, assistive navigation and automatic geocoding. Achieving such text detection is challenging due to complex backgrounds, non-uniform illumination, and variations in text font, size, and orientation. In this paper, we present a novel hybrid approach for detecting text robustly in natural scenes. We connect two text-detection methods in parallel structure: (1) a connected-component method and (2) a sliding-window method and outputs basically both results. The connected-component method generates text lines based on local relations of connected components. The sliding-window method consisting of a novel Hough Transform-based method generates text lines based on global structure. These two text-detection methods can output complementary results, which enables the system to detect various texts in natural scenes.

Online Handwritten Stroke Type Determination Using Descriptors Based on Spatially and Temporally Neighboring Strokes

We investigate the task of single-stroke classification into one of three classes (text, figure, or table rule lines). Individual strokes form handwriting structures such as text lines, figures, and tables in combination with peripheral strokes. To classify strokes using local contexts of neighborhood strokes, we propose a composite descriptor that represents in detail the relation between individual strokes and temporal and spatial neighborhood strokes. Evaluation of online handwritten documents written in English and in Japanese indicate that the proposed method more accurately classifies strokes than does the conventional method that employs shape-related features of a single stroke.

Fast and Memory Efficient Online Handwritten Strokes Retrieval Using Binary Descriptor

We propose a fast, memory-efficient online handwriting search method that uses handwritten strokes as a query and finds matches from among handwritten documents. The proposed method is language-independent, so not only words but also figures and symbols can be queried. We introduce a compact binary descriptor to lower computational resource load. A metric learning method enables derivation of a discriminative binary descriptor from directional densities of handwritten strokes. Experiments indicate that the proposed method is faster, more memory efficient, and exhibits more accurate search performance than a conventional method that employs directional densities. For 200 handwritten documents, the proposed method completed query searches within 1 s using a 1.3 GHz Tegra 3 CPU.