Prototype Pruning by Feature Extraction for Handwritten Mathematical Symbol Recognition
نویسندگان
چکیده
Successful mathematical handwriting recognition will require recognizers for large sets of handwritten symbols. This paper presents a recognition system for such handwritten mathematical symbols. The recognizer can provide a component of a handwritten interface for computer algebra systems such as Maple. Large sets of similar symbols present new challenges in the area of handwriting recognition, and we address these here. We use a pre-classification strategy, in combination with elastic matching, to improve recognition speed. Elastic matching is a model-based method that involves computation proportional to the set of candidate models. To solve this problem, we prune prototypes by examining character features. To this end, we have defined and analyzed different features. By applying these features into an elastic recognition system, the recognition speed is improved while maintaining high recognition accuracy.
منابع مشابه
A New Approach for Recognizing Offline Handwritten Mathematical Symbols Using Character Geometry
There are several problems in pattern recognition system like feature extraction problem and identification, pre-processing and classification problem etc. One of the application domains in pattern classification is handwritten character or symbolic recognition. Identifying handwritten characters is always a complex and challenging task for the researchers. Wide research has been done on the ch...
متن کاملNeural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten
Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...
متن کاملFeature Extraction Approach for Recognition of Handwritten Electrical Symbols
In this paper we consider a feature extraction approach for recognition of handwritten electrical symbols. The symbols are represented as a sequence of points. We apply a feature extraction technique to extract the most important features and then feed them for recognition to a Neural Network. We utilize a Learning Vector Quantization (LVQ) network and show its capability to recognize the symbo...
متن کاملSymbol detection in online handwritten graphics using Faster R-CNN
Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues...
متن کاملHMM-based handwritten symbol recognition using on-line and off-line features
This paper addresses the problem of recognizing on-line sampled handwritten symbols. Within the proposed symbol recognition system based on Hidden Markov Models different kinds of feature extraction algorithms are used analysing on-line features as well as off-line features and combining the classification results. By conducting writer-dependent recognition experiments, it is demonstrated that ...
متن کامل