Symbol Recognition System Research Articles

Low-Cost Online Handwritten Symbol Recognition System in Virtual Reality Environment of Head-Mounted Display

Virtual reality is an important technology in the digital media industry, providing a whole new experience for most people. However, its manipulation method is more difficult than the traditional keyboard and mouse. In this research, we proposed a new low-cost online handwriting symbol recognition system to accurately identify symbols by user actions. The purpose was low cost processing without requiring a server. Experimental results showed that the average success rate of recognition was 99.8%. The execution time averaged a significantly low 0.03395 s. The proposed system is, respectively, highly reliable and at a low cost. This implies that the proposed system is suitable for applications in real-time environments.

Symbol recognition system based on 3D stereo vision

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.

Incremental Learning of Hand Symbols Using Event-Based Cameras

Conventional cameras create redundant output especially when the frame rate is high. Dynamic vision sensors (DVSs), on the other hand, generate asynchronous and sparse brightness change events only when an object in the field of view is in motion. Such event-based output can be processed as a 1D time sequence, or it can be converted to 2D frames that resemble conventional camera frames. Frames created, e.g., by accumulating a fixed number of events, can be used as input for conventional deep learning algorithms, thus upgrading existing computer vision pipelines through low-power, low-redundancy sensors. This paper describes a hand symbol recognition system that can quickly be trained to incrementally learn new symbols recorded with an event-based camera, without forgetting previously learned classes. By using the iCaRL incremental learning algorithm, we show that we can learn up to 16 new symbols using only 4000 samples for each symbol and achieving a final symbol accuracy of over 80%. The system achieves latency of under 0.5s and training requires 3 minutes for 5 epochs on an NVIDIA 1080TI GPU.

建築設備図面への画像認識を取り入れた人工知能の適用に関する研究 (第３報) 構造適応型深層学習法による建築設備図面における図面記号の自動認識システムの構築

建築設備図面への画像認識を取り入れた人工知能の適用に関する研究 (第３報) 構造適応型深層学習法による建築設備図面における図面記号の自動認識システムの構築

On-line structure-based handwritten Chinese phonetic symbol recognition

This paper proposes a high-accuracy on-line structure-based handwritten Chinese phonetic symbol recognition system. Each reference symbol consists of visible and hidden line segments, where two line segments intersecting each other form an X-shape structure, and those forming a crucial T-shape structure mark a T-shape structure. The first-stage recognition performs the structure-based dynamic programming to match the input handwriting with reference symbols. The second-stage recognition performs the partialmerge dynamic programming to reevaluate the similarity between the input handwriting and the candidate symbols in a confusing set. The third-stage recognition performs the structure-constrained confirmation to determine the best candidate.

악보인식 전처리를 위한 강건한 오선 두께와 간격 추정 방법

이 논문에서는 모바일 기기상에서 카메라기반 악보인식을 위한 오선 두께와 오선 간격을 추정하는 전처리 기술을 제안한다. 캡쳐된 영상은 조명이나, 흐려짐, 저해상도 등의 많은 왜곡으로 인해 인식에 어려움이 있다. 특히 복잡한 배경을 가지고 있는 악보 영상인식의 경우 더욱 그렇다. 악보 기호 인식에서 오선 두께와 오선 간격은 인식에 큰 영향을 끼친다. 이들 정보는 이진화에도 사용되는데, 복잡한 배경을 가지고 있는 경우 일반적인 이진 영상은 오선 두께와 간격을 추정하는데 만족스럽지 못하다. 따라서 우리는 에지영상에서 런-길이 엔코딩 기술을 이용해 오선 두께와 간격 추정하는 강건한 알고리즘을 제안한다. 제안된 방법은 2단계로 구성되어 있다. 첫 번째 단계는 소벨 연산자에 의해 영역별로 에지 영상을 기반으로 오선 두께와 간격을 추정한다. 각 에지 영상의 열은 런-길이 엔코딩 알고리즘에 의해 기술된다. 두 번째 단계는 안정한 경로 알고리즘을 이용한 오선 검출과 오선 위치를 추적하는 적응적 LTH알고리즘을 이용한 오선 제거이다. 실험결과 복잡한 영상의 경우에도 강건함과 높은 인식률을 보였다. In this paper, we propose a robust pre-processing module for camera-based Optical Music Score Recognition (OMR) on mobile device. The captured images likely suffer for recognition from many distortions such as illumination, blur, low resolution, etc. Especially, the complex background music sheets recognition are difficult. Through any symbol recognition system, the staff line height and staff line space are used many times and have a big impact on recognition module. A robust and accurate staff line height and staff line space are essential. Some staff line height and staff line space are proposed for binary image. But in case of complex background music sheet image, the binarization results from common binarization algorithm are not satisfactory. It can cause incorrect staff line height and staff line space estimation. We propose a robust staff line height and staff line space estimation by using run-length encoding technique on edge image. Proposed method is composed of two steps, first step, we conducted the staff line height and staff line space estimation based on edge image using by Sobel operator on image blocks. Each column of edge image is encoded by run-length encoding algorithm Second step, we detect the staff line using by Stable Path algorithm and removal the staff line using by adaptive Line Track Height algorithm which is to track the staff lines positions. The result has shown that robust and accurate estimation is possible even in complex background cases.

Performance enhancement of online handwritten Tamil symbol recognition with reevaluation techniques

In this article, we aim at reducing the error rate of the online Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary support vector machine classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time-warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusion. Thirdly, statistics of specific features are proposed for resolving any confusions in vowel modifiers. The reevaluation approaches are tested on two databases (a) the isolated Tamil symbols in the IWFHR test set, and (b) the symbols segmented from a set of 10,000 Tamil words. The recognition rate of the isolated test symbols of the IWFHR database improves by 1.9 %. For the word database, the incorporation of the reevaluation step improves the symbol recognition rate by 3.5 % (from 88.4 to 91.9 %). This, in turn, boosts the word recognition rate by 11.9 % (from 65.0 to 76.9 %). The reduction in the word error rate has been achieved using a generic approach, without the incorporation of language models.

HBF49 feature set: A first unified baseline for online symbol recognition

As the rise of pen-enabled interfaces is accompanied with an increased number of techniques for recognition of pen-based input, recent trends in symbol recognition show an escalation in systems complexity (number of features, classifiers combination) or the over-specialization of systems to specific datasets or applications. Despite the importance of representation space in feature-based methods, few works focus on the design of feature sets adapted to a large variety of symbols, and no universal representation space was proposed as a benchmarking reference. We introduce in this work HBF49, a unique set of features for the representation of hand-drawn symbols to be used as a reference for evaluation of symbol recognition systems. An empirical constructive approach is adopted for designing this set of 49 simple features, able to handle a large diversity of symbols in various experimental contexts. An original effort is made for guaranteeing transparency of features design and reproducibility of experiments. We demonstrate that using off-the-shelf statistical classifiers, the HBF49 representation performs comparably or better than state-of-the-art results reported on eight databases of hand-drawn objects. We also obtain a good recognition performance for user-defined gestures that further attests the ability of HBF49 to deal with a great variety of symbols.

Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems

This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.

Research on self-learning mechanism in symbol recognition system of architecture drawings

Due to the uncertainty of the graphic symbol's representation in the recognition of the architecture drawings,the adaptability of the method of manually constructing templates is poor,which is entirely based on priori knowledge. To deal with the situation,the symbol recognition method including the mechanism of self-learning was proposed. The method merged self-learning and recognizing together. For different results of learning,such as omission and missing,it entered different learning steps,then completed the creation or update automatically. In this way,it improved the recognition rate without changing the program. Based on these,an architecture drawing recognition system was designed with the capacity of self-learning,and its effect was verified through some experiments.

An online handwritten music symbol recognition system

The objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. To accomplish these objectives, the following methods are proposed: (1) Two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) The strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. As a result, recognition rates of 97.60 and 98.80% were obtained in tests with strokes and music symbols, respectively.

Contextual System of Symbol Structural Recognition based on an Object-Process Methodology

We present in this paper a symbol recognition system for the graphic documents. This one is based on a contextual approach for symbol structural recognition exploiting an Object-Process Methodology. It uses a processing library composed of structural recognition processings and contextual evaluation processings. These processings allow our system to deal with the multi-representation of symbols. The different processings are controlled, in an automatic way, by an inference engine during the recognition process. The factual, strategic, and descriptive knowledge are structured according to an Object-Process Methodology of the considered recognition application. The representation of symbols are then adapted during the recognition process according to their classes. This original approach for knowledge structuring allows especially the strategic and descriptive knowledge combination which increases the recognition abilities of system. We present results of this system on the GREC2003 contest of symbol recognition.

An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.

Handprinted symbol recognition system

In this paper the design characteristics of a recognition system for unconstrained handprinted symbols are described. The targeted symbols and accuracy requirements from a mapping and charting environment posed problems requiring the development of new OCR techniques. The recognition system employs smart thinning algorithms to produce centerline thinned stick figure images from raster scanned characters. The recognition logic interacts with the feature extraction algorithms to extract just those topological, geometrical and local measurements that are needed to identify the character or to reject the character as unrecognizable. Results from a numeric character data base consist of some 7000 numerals collected from a broad spectrum of sources and yield an efficiency rate of 97%. Substitution error rate is 0.3% and rejection rate is 2.7%. Preliminary results for the recognition of alphabetic characters are also discussed.