SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Abstract

Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.

Synthesizing of Hand-drawn Electrical Circuits Using Machine Learning Techniques

“…Skeleton picture can give to distinguish end focuses which demonstrates an open line remain there. Figure 2 shows consequences of binarization and diminishing procedures [16]. Presumption (1) expresses that association lines are even and vertical lines.…”

Graph-Based Locality-Sensitive Circuit Sketch Recognizer

“…When the ink density is larger than a pre-defined threshold, it is assumed that there is a circuit component or connecting structure in this area. Similar ideas were also adopted in [11], [12], and [5]. One major concern of these methods is that they require a completed sketch image and is weak to recognize the circuit online.…”

“…Latter on, Sezgin et al [4] extended the probability graph model [3] to support interspersed drawings. Nevertheless, similar to [5], it is still influenced by stroke order to a large extend, since it is a learning-based approach and it is difficult to get a stroke order that never appears in the training set right.…”

“…Nevertheless, to ensure the time complexity over the number of strokes is not exponential, they had to make a constraint that all the circuit components must be drawn one by one without interspersing (e.g., drawing a stroke for one component, then turning to another component, and finally going back to finish the previous component). Methods like [3], [4] and [5] achieve stroke segmentation by training and inference schema with dynamic Bayes networks, i.e., learning from prior knowledge about how a specific component was drawn. On one hand, it is inconvenient to build a proper database for stroke segmentation training.…”

A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19

“…In [32], internet addition is measured using intelligent behaviour data analysis. In [33], stroke-based recognition of online handdrawn sketches of arrow connected diagrams and digital logic circuit diagrams. In [34], AI-based classification techniques are used to process EEG data collected during the visual short-term memory.…”