Showing papers on "Sketch recognition published in 1994"

Fundamentals in handwriting recognition

Abstract: 1: Introduction and overview of field.- Frontiers in handwriting recognition.- 2: Handwritten character recognition.- Historical review of theory and practice of handwritten character recognition.- Automatic recognition of handwritten characters.- Learning, representation, understanding and recognition of characters and words - an intelligent approach.- Digital transforms in handwriting recognition.- Pattern recognition with optimal margin classifiers.- 3: Handwritten word recognition.- On the robustness of recognition of degraded line images.- Invariant handwriting features useful in cursive script recognition.- Off-line recognition of bad quality handwritten words using prototypes.- Handwriting recognition by statistical methods.- Towards a visual recognition of cursive script.- A hierarchical handwritten word segmentation.- 4: Contextual methods in handwriting recognition.- Cursive words recognition: methods and strategies.- Hidden Markov models in handwriting recognition.- Language-level syntactic and semantic constraints applied to visual word recognition.- Verification of handwritten British postcodes using address features.- Improvement of OCR by language model.- An approximate string matching method for handwriting recognition post-processing using a dictionary.- 5: Neural networks in handwriting recognition.- Neural-net computing for machine recognition of handwritten English language text.- Cooperation of feedforward neural networks for handwritten digit recognition.- Normalisation and preprocessing for a recurrent network off-line handwriting recognition system.- 6: Architectures for handwriting.- Architectures for handwriting recognition.- 7: Databases for handwriting recognition.- Large database organization for document images.- 8: Signature recognition and verification.- A model-based dynamic signature verification system.- Algorithms for signature verification.- Handwritten signature verification: a global approach.- 9: Application of handwriting recognition.- Total approach for practical character recognition system development.- A pen-based music editor.

Object recognition and categorization using modal matching

Abstract: Recently the authors (1993) have proposed a new method for establishing correspondences and computing canonical descriptions, based on the idea of describing objects by their generalized symmetries, as defined by the object's vibration of deformation modes. In this paper they extend this method to the problems of object recognition and categorization. Examples of recognizing hand tools and airplanes are shown. >

Appearance-Based 3D Object Recognition

Abstract: General, three-dimensional object recognition is still an unsolved problem. A major handicap in many systems is the use of standard point and straight-line-segment features for recognition. We believe that general object recognition can only be accomplished by utilizing the appropriate sensors for each object class and the appropriate features that can be reliably extracted using those sensors. We also believe that the analysis of complex scenes will require an active system. In this paper we define a new representation called an appearanced-based model and discuss its use for hypothesize-and-test object recognition in an active environment.

A two-level model-based object recognition technique

Abstract: Object recognition is the problem of detecting the presence and determining the pose of a set of known objects in given images. For some applications, the known objects may be composed of identical components. The relations among these components can be exploited to improve recognition accuracy. This paper introduces a two-level model for object recognition which reduces redundant work due to objects with identical components by explicitly specifying the components and the relations among them. Using automatic analysis of music scores as example, an empirical study is presented, demonstrating the effectiveness and properties of the technique. >

Towards a Robust Physics-Based Object Recognition System

Abstract: A successful 3D object recognition system must take into account imperfections in the input data, due for example to fragmentation or sensor noise. In this paper we propose a methodology for robust 3D object recognition using uncertain image data. In particular, we present a method capable of achieving acceptable performance in the presence of both segmentation problems and sensor uncertainty, thus eliminating the need for ad hoc heuristics. The proposed method is based upon the use of probabilistic models suggested by the underlying physics processes. These models are statistically validated and tested under controlled experimentation.

Pattern recognition based on information theory principles

Abstract: One of the main problems faced in the development of pattern recognition algorithms is assessment of their performance. This paper describes the development of a novel technique for the assessment of information content of 2-D patterns encountered in practical pattern recognition problems. The technique is demonstrated by itsapplication to multi-font typed character recognition. In this work we firstly developed an information model applicable to any pattern, and its elaboration to measure recognition performance, and secondly we used this model to derive parameters such as the resolution required to distinguish between the patterns. This has resulted in a powerful method for assessing the perfoimance of any pattern recognition system.Keywords: pattern recognition, information theory, character recognition, recognition information 1. INTRODUCTION Pattern Recognition is one of the fastest growing scientific areas with applications across a wide variety of disciplines. The tasks of pattern recognition are basically to remove the need for a trained operator to perform therecognition, or to enable recognition to be performed that would otherwise be impossible [1]. When examining a

Recognition by using an active/space-variant sensor

Abstract: The problem of object recognition is addressed. In the literature this task has been generally considered in a "passive" perspective, where everything is static and there is no definite relation between the object and its environment. We propose an "active" approach for object recognition, based on the capability of the observer to move and give a better description of the object under consideration and also to take advantage of the relations between the objects and the environment. This can be accomplished at the task level and at the sensor level. The face recognition problem, based on the face-space approach, is considered to demonstrate the advantage of adopting an active retina to sample the face, build a database and perform the recognition task. By using an active space-variant retina the size of the database is considerably reduced and consequently the processing time for recognition. A comparative experiment using the active and static approach is presented. >

Document analysis systems: problems and perspectives, a real case study

Abstract: We deal with the development of intelligent forms processing systems that allow one to automate the manual operations in existing paper-based systems, ensuring the same reading capabilities of humans. For this purpose, the traditional research on character recognition has expanded its frontiers to novel goals in the field of advanced preprocessing, layout understanding, typewritten and handwritten character and word recognition, and intelligent postprocessing and verification of document authenticity. >

Adaptive logic networks for machine-printed character recognition

Abstract: In this paper we investigate an approach to the recognition of machine-printed characters using the Adaptive Logic Network (ALNs). Traditional approaches to character recognition such as Nearest Neighbor Classifiers, Template Matching Schemes and Artificial Neural Networks are very computationally expensive. We have implemented a relatively inexpensive recognition module based on ALNs. This type of network achieves recognition results nearly comparable to those obtained using more conventional methods.

Using two-dimensional subspaces gained by intersections to recognize three-dimensional objects

Abstract: This paper presents a model-based object recognition system for three-dimensional objects with an automatic generation of the models. Due to the fact that these models are generated by algorithms and do not look like CAD models, the well-known restrictions of standard model-based object recognition systems do not apply to this method. >

A system for recognition and modification of machining features on prismatic components

Abstract: A recognition process of the features in a part model resorts to the knowledge of an application engineer. The knowledge is encoded as rules of the recognition procedure at the beginning that are applied to the part model during the recognition process. Such a human interaction is difficult to control in extracting the intended features because the intended features by the application engineer may change from one engineer to the other and the external situations. Instead, we treat the result of the recognition process as a rough extraction and allow the user interactively modify the result. In this paper, we present a feature recognition system where the user can inspect the result of the recognition and delete recognized features interactively.

Pattern recognition in HCI: a viable approach?: a CHI '94 workshop

Abstract: Over recent years there has been a steadily increasing interest in the use of pattern recognition techniques (in particular neural networks) in human-computer interaction This has focused largely on applications such as speech and gesture recognition where the problems are quite well understood but has also considered less traditional domains such task analysis, user modelling and information retrieval. In 1991 we held a workshop at CHI entitled "Neural Networks and Pattern Recognition in CHI" [1] where the aim was to consider the range of possible applications for these techniques within HCI. It was a success, leading to the publication of a book on subject [2] which has sold well internationally and is still in demand, but three years on pattern recognition seems no nearer to being accepted within the mainstream of HCI, remaining largely associated with speech and gesture.

Context-driven text recognition by means of dictionary support

Abstract: This paper presents an alternative method for typed character recognition by way of the textual context. The approach here is word-oriented, and uses no a priori knowledge about typical appearance of characters. It leads back to an approach suggested by R. G. Casey where text recognition is considered as solving a substitution cipher, or cryptogram. Character images are considered only in order to distinguish or group (cluster) them. The recognition information used is provided by dictionaries. The overall procedure can be divided into three principle steps: (1) a ciphertext like symbolic representation of the text is generated. (2) in an initialization phase only a few but reliable word recognitions are striven for. The resulting partial symbol-character assignments are sufficient to initiate the following relaxation of the recognition process as the third step. Whereas Casey uses several ambiguous alternatives for word recognition, the approach here is based on acquiring a few, but reliable, recognition alternatives. Thus, instead of a spell check program, a dictionary with a heuristic-driven look- up control combined with an appropriate access mechanism is used.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.