Gyeonghwan Kim

A lexicon driven approach to handwritten word recognition for real-time applications

A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi.

A robust license-plate extraction method under complex image conditions

A robust approach for extracting car license plate from images with complex background and relatively poor quality is presented. The approach focuses on dealing with images taken under weak lighting condition. The proposed method is divided into two steps: 1) searching candidate areas from the input image using gradient information, and 2) determining the plate area among the candidates and adjusting the boundary of the area by introducing a plate template. A set of experiments has been performed to prove the robustness and accuracy of the approach. For many images collected from a large underground parking place the result shows that 90% of them are correctly segmented.

An architecture for handwritten text recognition systems

Abstract. This paper presents an end-to-end system for reading handwritten page images. Five functional modules included in the system are introduced in this paper: (i) pre-processing, which concerns introducing an image representation for easy manipulation of large page images and image handling procedures using the image representation; (ii) line separation, concerning text line detection and extracting images of lines of text from a page image; (iii) word segmentation, which concerns locating word gaps and isolating words from a line of text image obtained efficiently and in an intelligent manner; (iv) word recognition, concerning handwritten word recognition algorithms; and (v) linguistic post-pro- cessing, which concerns the use of linguistic constraints to intelligently parse and recognize text. Key ideas employed in each functional module, which have been developed for dealing with the diversity of handwriting in its various aspects with a goal of system reliability and robustness, are described in this paper. Preliminary experiments show promising results in terms of speed and accuracy.

Chaincode contour processing for handwritten word recognition

Contour representations of binary images of handwritten words afford considerable reduction in storage requirements while providing lossless representation. On the other hand, the one-dimensional nature of contours presents interesting challenges for processing images for handwritten word recognition. Our experiments indicate that significant gains are to be realized in both speed and recognition accuracy by using a contour representation in handwriting applications.

Handwritten phrase recognition as applied to street name images

A phrase recognition method for recognition of street name images is presented in this paper. Some of the challenges posed by the problem are: (i) patron errors, (ii) non-standardized way of abbreviating names, and (iii) variable number of words in a street name image. A neural network has been designed to segment words in a phrase, using distance between components and style of writing. Experiments show perfect word segmentation performance of 85%. Substring matching is attempted only between the main body of a lexicon entry and the word segments of an image. Efforts to reduce computational complexity are successfully made by the sharing of character segmentation results between the segmentation and recognition phases. 83% phrase recognition accuracy was achieved on a test set.

Handwritten word recognition for real-time applications

A fast handwritten word recognition system for real time applications is presented. Preprocessing, segmentation and feature extraction are implemented using chain code representation. Dynamic matching between each character of a lexicon entry and segment(s) of input word image is used for ranking words in the lexicon. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform for lexicon size of 10. A top choice performance of 96% is achieved on a database of postal words captured at 212 dpi.

An approach for locating segmentation points of handwritten digit strings using a neural network

An approach for segmentation of handwritten touching numeral strings is presented in this paper. A neural network has been designed to deal with various types of touching observed frequently in numeral strings. A numeral string image is split into a number of line segments while stroke extraction is being performed and the segments are represented with straight lines. Four types of primitive are defined based on the lines and used for representing the numeral string in more abstractive way and extracting clues on touching information from the string. Potential segmentation points are located using the neural network by active interpretation of the features collected from the primitives. Also, the run-length coding scheme is employed for efficient representation and manipulation of images. On a test set collected from real mail pieces, the segmentation accuracy of 89.1% was achieved, in image level, in a preliminary experiment.

FPGA-based fast image warping with data-parallelization schemes

In this paper, we present an FPGA-based fast image warping method by applying data parallelization schemes. The parallelization of accesses to pixels relieves not only latency problem of the warping, but also bandwidth requirements of off-chip memory. The LUT data parallelization scheme efficiently replaces parallel arithmetic operations with neither of increased memory size for LUT entries nor clock frequency. Two implementations with different characteristics prove the effectiveness and efficiency of the proposed method.

A segmentation and recognition strategy for handwritten phrases

A segmentation and recognition method for handwritten phrases, such as street names, is presented in this paper. Some of the challenges posed by the problem are: (1) identifying correct word gaps from character gaps and (2) minimization of computational complexity during the recognition of potential words. A trainable word segmentation scheme using a neural network is introduced. The network learns the type of spacing (including size) that one should expect between different pairs of characters in handwritten text. The concept of variable duration, which is obtained during the training phase of a word recognition engine we have developed, is expanded to reduce the computational complexity which has been a serious concern in this type of application.

Robust Estimation of Motion Blur Kernel Using a Piecewise-Linear Model

Blur kernel estimation is a crucial step in the deblurring process for images. Estimation of the kernel, especially in the presence of noise, is easily perturbed, and the quality of the resulting deblurred images is hence degraded. Since every motion blur in a single exposure image can be represented by 2D parametric curves, we adopt a piecewise-linear model to approximate the curves for the reliable blur kernel estimation. The model is found to be an effective tradeoff between flexibility and robustness as it takes advantage of two extremes: (1) the generic model, represented by a discrete 2D function, which has a high degree of freedom (DOF) for the maximum flexibility but suffers from noise and (2) the linear model, which enhances robustness and simplicity but has limited expressiveness due to its low DOF. We evaluate several deblurring methods based on not only the generic model, but also the piecewise-linear model as an alternative. After analyzing the experiment results using real-world images with significant levels of noise and a benchmark data set, we conclude that the proposed model is not only robust with respect to noise, but also flexible in dealing with various types of blur.Blur kernel estimation is a crucial step in the deblurring process for images. Estimation of the kernel, especially in the presence of noise, is easily perturbed, and the quality of the resulting deblurred images is hence degraded. Since every motion blur in a single exposure image can be represented by 2D parametric curves, we adopt a piecewise-linear model to approximate the curves for the reliable blur kernel estimation. The model is found to be an effective tradeoff between flexibility and robustness as it takes advantage of two extremes: (1) the generic model, represented by a discrete 2D function, which has a high degree of freedom (DOF) for the maximum flexibility but suffers from noise and (2) the linear model, which enhances robustness and simplicity but has limited expressiveness due to its low DOF. We evaluate several deblurring methods based on not only the generic model, but also the piecewise-linear model as an alternative. After analyzing the experiment results using real-world images with significant levels of noise and a benchmark data set, we conclude that the proposed model is not only robust with respect to noise, but also flexible in dealing with various types of blur.