Yoshinobu Hotta

Handwritten Chinese address recognition

A handwritten Chinese address recognition (HCAR) system is proposed in this paper. Handwritten Chinese address recognition is a difficult problem. Handwritten Chinese characters are characterized by large vocabulary, complicate structure, irregular distortion and touching characters etc. The proposed approach takes good advantage of Chinese address knowledge, and applies key character extraction and holistic word matching to solving the problem. Different from conventional approach, proposed approach can avoid the character segmentation error successfully. Experimental results show the proposed approach is very effective.

Camera based degraded text recognition using grayscale feature

As the rapid progress of digital imaging technology, camera based character recognition receives more and more attentions. One challenge in camera based OCR is the recognition for degraded text. Conventional OCR engines usually recognize on binary image. However, the performance drops dramatically as the degradation level increases. In this paper, a new recognition method is proposed to recognize degraded character based on dual eigenspace decomposition and synthetic degraded data. Then, the degraded character string is segmented by the combination of binary and grayscale analysis. Experiments on single character and text string recognition prove the effectiveness of our method.

Local Consistency Constrained Adaptive Neighbor Embedding for Text Image Super-Resolution

This paper proposes a robust single-image super-resolution method for enlarging low quality camera captured text image. The contribution of this work is twofold. First, we point out the non-local reconstruction problem in neighbor embedding based super-resolution by statistical analysis on an empirical data set. Second, we introduce a local consistency constraint to explicitly regularize the linear reconstruction process, and adaptively generate the most possible candidates for the high-resolution image patch. For the non-consistent candidates, we rely on its adjacent overlapping patches for capability verification. Experimental results demonstrate that our solution produces visually pleasing enlargements for various text images.

A Dual Pass Video Stabilization System Using Iterative Motion Estimation and Adaptive Motion Smoothing

In this paper, we propose a novel dual pass video stabilization system using iterative motion estimation and adaptive motion smoothing. In the first pass, the transformation matrix to stabilize each frame is returned. The global motion estimation is carried out by a novel iterative method. The intentional motion is estimated using adaptive window smoothing. Before the beginning of the second pass, we obtain the optimal trim size for a specific video based on the statistics of the transformation parameters. In the second pass, the stabilized video is composed according to the optimal trim size. Experimental results show the superior performance of the proposed method in comparison to other existing methods.

Global interpolation in the segmentation of handwritten characters overlapping a border

The global interpolation (GIM) we propose evaluates segment pattern continuity and connectedness to produce characters with smooth edges while interpreting blank or missing segments, e.g., in extracting a handwritten character overlapping a border, correctly. Characters contacting a border, for example, are extracted after the border itself is labeled and removed. The absence of character segments is then interpolated based on segment continuity. Interpolated segments are relabeled and checked for matching against the original labeled pattern. If a match cannot be made, segments are reinterpolated until they can be identified. Experimental results show that global interpolation interprets the absence of character segments correctly and generates with smooth edges.<<ETX>>

Text Detection in Images Based on Grayscale Decomposition and Stroke Extraction

A method of detecting text regions in images which combines grayscale decomposition and stroke extraction is proposed. By checking the consistency of the two text features, text-like connected components are grouped together to generate text line regions in the processed image. It shows good performance on efficiently detecting image text rendered in relatively complex backgrounds.

Video caption duration extraction

Caption detection in the video is an active research topic in recent years. In the conventional methods, one of most difficult problems is to effectively and quickly extract the durations of the different-size captions in the complex background. To solve this problem, a novel and effective method is presented to locate and track the captions in the video. The main contributions are: (1)present a multi-scale Harris-corner based method to detect the initial position of the caption (2)propose the SGF (Steady Global Feature) to determine the caption duration. Extensive experiments demonstrate the effectiveness of the proposed method.

A Fast Caption Detection Method for Low Quality Video Images

Captions in videos are important and accurate clues for video retrieval. In this paper, we propose a fast and robust video caption detection and localization algorithm to handle low quality video images. First, the stroke response maps from complex background are extracted by a stoke filter. Then, two localization algorithms are used to locate thin stroke and thick stroke caption regions respectively. Finally, a HOG based SVM classifier is carried out on the detected results to further remove noises. Experimental results show the superior performance of our proposed method compared with existing work in terms of accuracy and speed.

Enhancement of camera-based whiteboard images

Quality of camera-based whiteboard images is highly related to the light environment and the writing effect of the content. Specular reflection and low contrast reduce the readability of captured whiteboard images frequently. A novel method is proposed to enhance camera-based whiteboard images in this paper. The images are enhanced by removing the highlight specular reflection to improve the visibility and emphasizing the content to improve the readability of the whiteboards. The method can be practically embedded in mobile devices with image capturing cameras.

Handwritten Numeral Recognition Using Personal Handwriting Characteristics Based On Clustering Method

To improve recognition rate, it is important not only to utilize one character feature but personal handwriting characteristics. This paper realizes above approach based on our investigation result that characters written by the same writer have similar shapes and that there are several shapes even in the same category. In our method, clustering method is used to absorb the variance of character shapes in the category. First, character recognition for each character is executed. Next, misrecognized character candidates are extracted as isolated cluster by within-category clustering. Then, recognition results of the extracted characters are amended by between-category clustering which evaluates the distance between the cluster composed of misrecognized characters and the cluster composed of correctly recognized characters in every categories. Finally, experimental results shows that recognition rate is remarkably improved by our method.