Automatic Abstraction of Combinational Logic Circuit from Scanned Document Page Images (original) (raw)
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Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm
IEEE Access
Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system. INDEX TERMS Handwritten logic circuit, deep learning, YOLO, boundary tracking.
Handwritten Circuit Schematic Detection and Simulation using Computer Vision Approach
2014
Hand-drawn sketch is a natural and direct way to express people’s thought and meaning and is of common use in many different fields. Document image analysis is an active and challenging area of research in computer vision. Documents comprise of text and graphics. Machine recognition of hand-written text involves languages, mathematical symbols, digits, medical symbols etc. Machine recognition of hand-drawn graphical entities such as circuit diagrams, flow charts, tables, etc. will add another dimension to human computer interaction. In this work we propose a system of offline circuit recognition and simulation using digital image processing. The proposed model consists of all possible components of a diagram recognition system, such as segmentation, feature extraction, classification and redrawing and repositioning. Then we use this circuit for simulation purpose by substituting values for each component to generate output waveforms/characteristics graph. Keywords— handwritten circu...
Handwritten Electric Circuit Diagram Recognition: An Approach Based on Finite State Machine
International Journal of Machine Learning and Computing, 2019
In this paper we propose a method for recognizing hand drawn electronic circuit diagrams. The proposed method first detect and classify each components present in the hand drawn circuit diagram. For the purpose of component recognition, we have constructed the feature vector by combining Local Binary Pattern (LBP) and statistical features based on pixel density. Classification of components is done by using support vector machine (SVM) classifier. Upon detection and recognition of components, the proposed method subsequently uses the position and sequence of arrangement of components to determine the type of circuit. For the purpose of establishing the sequence of components we have used finite state machine. The proposed method represents the sequence of recognized components as a string. This string representation of circuit is fed to a Finite State Machine (FSM) to detect type of circuit. The proposed method has been tested on about 100 hand written circuit diagrams of varying complexities and of different types. The proposed component detection method gives over 99% accuracy whereas, the circuit recognition method has recognition rate of over 85% recognition rate for the circuit type recognition.
Detection of circuit components on hand-drawn circuit images by using faster R-CNN method
International Advanced Researches and Engineering Journal, 2021
In this study, one of deep learning methods, which has been very popular in recent years, is employed for the detection and classification of circuit components in hand-drawn circuit images. Each circuit component located in different positions on the scanned images of hand-drawn circuits, which are frequently used in electrical and electronics engineering, is considered as a separate object. In order to detect the components on the circuit image, Faster Region Based Convolutional Neural Network (R-CNN) method is used instead of conventional methods. With the Faster R-CNN method, which has been developed in recent years to detect and classify objects, preprocessing on image data is minimized, and the feature extraction phase is done automatically. In the study, it is aimed to detect and classify four different circuit components in the scanned images of hand-drawn circuits with high accuracy by using the Python programming language on the Google Colab platform. The circuit components to be detected on the hand-drawn circuits are specified as resistor, inductor, capacitor, and voltage source. For the training of the model used, a data set was created by collecting 800 circuit images consisting of hand drawings of different people. For the detection of the components, the pretrained Faster R-CNN Inception V2 model was used after fine tuning and arrangements depending on the process requirements. The model was trained in 50000 epochs, and the success of the trained model has been tested on the circuits drawn in different styles on the paper. The trained model was able to detect circuit components quickly and with a high rate of performance. In addition, the loss graphics of the model were examined. The proposed method shows its efficiency by quickly detecting each of the 4 different circuit components on the image and classifying them with high performance.
IJERT-Logical Symbol Recognition using Normalized Chaincode and Density Features
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/logical-symbol-recognition-using-normalized-chaincode-and-density-features https://www.ijert.org/research/logical-symbol-recognition-using-normalized-chaincode-and-density-features-IJERTV3IS120611.pdf Mathematical expressions are used in many scientific documents. The recognition of handwritten mathematical symbols is one of the most challenging research areas in the field of image processing and pattern recognition. The difficulties of handwritten mathematical symbol recognition is due to variability of the symbols and their two dimensional structure. This paper implements feature extraction algorithms and analyzes the performance of a recognizer. The strength of the proposed approach is its methods for efficient preprocessing and feature extraction. In this work, mathematical logical symbol recognition system has been developed by using support vector machine (SVM) and back propagation neural network. By applying combinations of normalized chain code, density, image invariance features to SVM and Artificial Neural Network (ANN), a high recognition accuracy is attained. A database of 2000 symbols was created. Preprocessing techniques are used to remove noise and thinning of the image, and features are extracted. The recognition rate for handwritten mathematical logical symbols is observed to be high when SVM is used.
A reconfigurable printed character recognition system using a logic synthesis tool
Proceedings. 24th EUROMICRO Conference (Cat. No.98EX204), 1998
In recent years functional decomposition methods, widely known to logic synthesis researchers are being applied in diverse fields such as Machine Learning [ 141 [ 171, Knowledge Discovery [ 161 [ 171, Information Systems [ 51 [7] [ 131 and Image Compression [lo]. This paper presents a novel method for recognising machine printed characters and character images using functional decomposition. The methods found in literature [9] [20] try to find some characteristics of an image and apply different computational intelligence techniques to match them to a character. Each character or image is viewed as set of conditions with a corresponding set of decisions. This paper shows functional decomposition [4][6] as a tool in determining the characters by considering less number of conditions. Moreover, the structures produced by functional decomposition are easily implementable by FPGAs and therefore can be quickly reconfigured to suit a different set of characters. The problem of character recognition is analogous to decision making in information systems. The decision table (DT) generated from a set of characters is functionally decomposed and intermediate decision rules are generated. The advantage of the proposed method is in the analysis of the character recognition process using the intermediate conditions and decisions.
AN EFFICIENT ARCHITECTURE FOR RECOGNITION OF SKETCHED ELECTRICAL CIRCUIT
It is a typical practice for architects to invest a lot of energy setting down beginning ideas utilizing pencil and paper. Regularly, it requires extra time to change the work into electronic media as specialized drawings. A portrayal acknowledgment system would spare specialists much time redrawing these in specialized programming. In this anticipate we will perceive outlined electrical circuit images. We need to accomplish a trainable electronic outlined circuit recognizer that has quick reaction time, high precision and simple extensibility to new segment. We will utilize PCA based picture preprocessing and watershed division technique to portion circuit sketch.
Feature Extraction Approach for Recognition of Handwritten Electrical Symbols
2000
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 symbols.
Integration, the VLSI Journal, 2006
This paper presents FROSTY, a computer program for automatically extracting the high-level structural representation of a large-scale digital CMOS circuit from its transistor-level netlist and a library of subcircuit descriptions. To handle the complexity and diversity of industrial circuits, FROSTY combines traditional structural recognition and pattern matching methods into a two-step extraction process. First, logic structures based on channel-connected-components are recognized from a circuit netlist and from all library subcircuits, and are condensed into ''macro devices'' or called logic gates. This leads to hybrid netlists that contain the recognized logic gates and remaining transistors. Then annotated graphs representing the connectivity and properties of logic gates and remaining transistors are constructed. Compared to transistor-level netlists, these hybrid graphs are much smaller in size, more distinguishable in structure, and are thus more suitable for labeling-based pattern matching. An efficient pattern matching algorithm is then applied to extract the high-level structural representation from these condensed circuit graphs. FROSTY has demonstrated to be orders of magnitude faster than the pattern matching-based extraction program SubGemini, and can extract entire industrial designs with several hundreds of