16442037
Handwritten Bank Check Recognition Of Courtesy Amounts
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates.
2857808
A knowledge-based segmentation algorithm for enhanced recognition of handwritten courtesy amounts
Abstract A knowledge-based segmentation algorithm to enhance recognition of courtesy amounts on bank checks is proposed in this paper. This algorithm uses multiple contextual cues to enhance segmentation and recognition. The system described extracts context from the handwritten numerals and uses a syntax parser based on a deterministic finite automaton to provide adequate feedback to enhance recognition. Further feedback is provided by a simple legal amount decoder that determines word count and recognizes several key words (e.g. thousand and hundred). This provides an additional semantic constraint on the dollar section. The segmentation analysis module presented is capable of handling a number of commonly used styles for courtesy amount representation. Both handwritten and machine written courtesy and legal amounts were utilized to test the efficacy of the preprocessor for the check recognition system described in this paper. The substitution error was reduced by 30–40% depending on the input check mix.
31996759
Analysis and Design of Parallel Thinning Algorithms - A Generic Approach
In this paper, a generic approach for the analysis and design of parallel thinning algorithms is presented. The procedures for designing 4-subcycle/iteration, 2-subcycle/iteration, and 1-subcycle/iteration thinning algorithms are developed. The experimental results show that these thinning algorithms designed by proposed approach preserve image connectivity, produce thinner results, and obtain faster speed than many existing key thinning algorithms. As an example, a 1-subcycle/iteration algorithm and its experimental results were illustrated, which is in general about two times faster than others in the literature.
207116582
A Thinning Algorithm Based on the Force Between Charged Particles
A new thinning algorithm based on the well known concept of the force of attraction or repulsion between charged particles is presented. This algorithm generates connected skeletons which preserve the shape and end-points of the original patterns. Its performance is experimentally compared with four other known algorithms published in the literature. For the sake of comparison, a reasoned set of test data is introduced. The results of our comparison reveal that the proposed CPM (Charge Particle Method) algorithm is almost as fast as the fastest of those compared. For thin images obtainable from low resolution scanners or coarse scanned images, the CPM algorithm is the fastest.
198169122
An adaptive Modular Neural Network with Application to unconstrained Character Recognition
2131320
An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals
Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."
26841509
From inheritance relation to nonaxiomatic logic
Abstract A nonaxiomatic reasoning system is an adaptive system that works with insufficient knowledge and resources. At the beginning of the paper, three binary term logics are defined. The first is based only on an inheritance relation. The second and the third suggest a novel way to process extension and intension, and they also have interesting relations with Aristotle's syllogistic logic. Based on the three simple systems, a nonaxiomatic logic is defined. It has a term-oriented language and an experience-grounded semantics. It can uniformly represent and process randomness, fuzziness, and ignorance. It can also uniformly carry out deduction, abduction, induction, and revision.
6309219
Contextual browsing for highlights in sports video
A novel highlight browsing approach for sports videos, based on contextual cue categorization, is proposed in this paper. Contextual cues are those salient audio and visual cues around highlight segments, and they are also informative and attractive to audiences. With further analysis and categorization of these contextual cues, the user could access highlights more flexibly and effectively. An experimental system is built for broadcasting diving videos in our current work, in which contextual cues such as slow-motion replays and game statistics information are extracted and categorized for highlight browsing. An efficient algorithm for slow-motion replay detection and categorization in diving videos is also proposed. Experimental results on 2 hours broadcasting diving videos have demonstrated the effectiveness and efficiency of the proposed approach.
11895032
A Signal-Dependent Quadratic Time Frequency Distribution for Neural Source Estimation
A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain.
487167
On Self-organising Diagnostics in Impact Sensing Networks
Structural health management (SHM) of safety-critical structures requires multiple capabilities: sensing, assessment, diagnostics, prognostics, repair, etc. This paper presents a capability for self-organising diagnosis by a group of autonomous sensing agents in a distributed sensing and processing SHM network. The diagnostics involves acoustic emission waves emitted as a result of a sudden release of energy during impacts and detected by the multi-agent network. Several diagnostic techniques identifying the nature and severity of damage at multiple sites are investigated, and the self-organising maps (Kohonen neural networks) are shown to outperform the standard k-means algorithm in both time- and frequency domains.
14294426
Gigabit packet classification by using lookahead caching
Hashing is a widely used method to perform fast lookup. Several schemes have been proposed to support Internet lookup that includes IP lookup and packet classification. Rectangular search is a well-known packet classification scheme based on multiple hash accesses for different filter length. It shows good scalability with respect to the number of filters; however, the lookup performance is not satisfactory. For example, through experiments, each packet classification takes about 40 hash accesses in a 100,000-filter database and each hash access may take more than one memory access. Obviously, this is insufficient to provide gigabits throughput. We proposed a novel "lookahead caching" which can significantly improve the performance of the hash-based algorithm. The basic idea is to find out the unmatched case for each incoming packet, thus it is different from the traditional caching mechanism. The experimental results indicate that the proposed scheme can improve the performance by a factor of two. The scheme can be further enhanced using parallel processing.