Amardeep Singh

Detection and segmentation of lines and words in Gurmukhi handwritten text

The scanned text image is a non editable image though it has the text but one can not edit it or make any change, if required, to that scanned document. This provides a basis for the optical character recognition (OCR) theory. OCR is the process of recognizing a segmented part of the scanned image as a character. The overall OCR process consists of three major sub processes like pre processing, segmentation and then recognition. Out of these three, the segmentation process is the back bone of the overall OCR process. We can say that the segmentation process is the most significant process because if the segmentation is incorrect then we can not have the correct results; it is just like garbage in and garbage out. But it is not an easy job, because segmentation is one of the complex processes. It is more difficult if the document is handwritten because in that case only few points are there which can be used to make segmentation. In this paper, we formulate an approach to segment the scanned document image. As per this approach, initially this considers the whole image as one large window. Then this large window is broken into less large windows giving lines, once the lines are identified then each window consisting of a line is used to find a word present in that line and finally to characters. For that purpose we used the concept of variable sized window, that is, the window whose size can be adjusted according to needs. This concept was implemented and results were analyzed. After the analysis the same concept was modified and finally tried on different documents and we got good reasonable results.

Algorithm to Detect and Segment Gurmukhi Handwritten Text into Lines, Words and Characters

The output of a scanner is a non editable scanned text image. Though the text is visible but one can neither edit it nor make any change, if required. This provides a basis for the optical character recognition (OCR) theory. OCR consists of generally three major phase; pre processing after image acquisition, segmentation and recognition. The segmentation process is the most crucial phase. The output of this phase decides the outcome of recognition phase. If this output is right then recognition phase would give the right output otherwise not. In this paper, we provide an algorithm which is used to segment the scanned document image as a lines, words and characters. The coordinates of line detected are used to find the word position present in that line. Finally, these words position coordinates are used to find characters present in the word. To detect lines and words, one module is proposed which is used to find both. For character detection, the reverse engineering is used, i.e. one part is extracted from the word present in the line. This extracted part is checked whether it has some meaningful symbol (as per Gurmukhi script). If it has then the extracted part is marked and written in the file, otherwise the extracted part is readjusted to find the symbol. This overall concept was implemented, and got encouraging results.

Static vision based Hand Gesture recognition using principal component analysis

Gesture recognition turns up to be important field in the recent years. Communication through gestures has been used since early ages not only by physically challenged persons hut nowadays for many other applications. Interacting with physical world using expressive body movements is much easier and effective than just speaking As most predominantly hand is used to perform gestures. Hand Gesture Recognition have been widely accepted for numerous applications such as human computer interactions, robotics, sign language recognition, etc Hand Gesture recognition techniques are basically divided into vision based and sensor based techniques. This paper focuses on vision based hand gesture recognition system by proposing a scheme using a database-driven hand gesture recognition based upon skin color model approach and thresholding approach along with an effective template matching using PCA. Initially, hand region is segmented by applying skin color model in YCbCr color space. In the next stage otsuthresholding is applied to separate foreground and background. Finally, template based matching technique is developed using Principal Component Analysis (PCA) for recognition. The system is tested with 4 gestures with 5 different poses per gesture from 4 subjects making 20 images per gesture and shows 91.25% average accuracy and 0.098251 seconds average recognition time and finally confusion matrix is drawn.

Detection and segmentation of Handwritten Text in Gurmukhi Script using Flexible Windowing

To make use of non editable scanned image of thedocument, one has to pass through the recognition process. The recognition process consists of sub processes like pre processing, segmentation and then recognition. Segmentation process is the most significant process because if the segmentation is incorrect then we can not have the correct result, it is just like garbage in and garbage out. On the same time it is one of the complex processes too. It is more difficult if the document is handwritten because in that case only few points are there which can be used to make segmentation. In this paper, we tried to formulate a procedure which is used to segment the scanned document image into lines then into words and finally to characters. For that purpose we used the concept of flexible window, that is, the window whose size can be adjusted according to needs. One module is designed to find the window. Same module is used to get the different types of outputs (lines, words, and characters) with a little bit adjustment to parameters passing as well as to the procedure itself. The concept was applied to different documents and we got good reasonable results.

Character Segmentation in Gurumukhi Handwritten Textusing Hybrid Approach

the researchers to think about the optical character recognition (OCR). OCR is the process of recognizing a segmented part of the scanned image as a character. OCR process consists of three major sub processes-pre processing, segmentation and then recognition. Out of these three, the segmentation process is the most important phase of the overall OCR process. It is the most significant process because if the output of segmentation phase is incorrect then we can not expect the correct results; it is just like garbage in and garbage out. But on the same time, segmentation is complex too. If the document is handwritten then the situation becomes more cumbersome, because in that case only few points are there which can be used to make segmentation. In this paper, we formulate an algorithm to segment the scanned document image as a character. As per our earlier published work, the information about the lines and words within each line is written in a data file. According to proposed algorithm, one part is extracted from the word present in the line. This extracted part is checked whether it has some meaningful symbol (as per Gurumukhi script). If it has then the extracted part is marked and written in the file, otherwise the extracted part is readjusted to find the symbol. For classification, we have used hybrid approach which consists of water reservoir and feature extraction approach. This concept was implemented and got good reasonable results.

DNA computing approach for automated test pattern generation for digital circuits

Testing of digital circuits is a compute intensive problem. This article deals with the problem of automated test pattern generation for large digital circuits. A new evolutionary approach based on DNA computing is presented, which exploits the computational power of DNA molecules to solve the problem. A Boolean formula in conjunctive normal form is extracted from the circuit under test and then the proposed algorithm based on DNA computing is used to find the solution satisfying that formula. Exploiting the massive parallelism and recombination properties of DNA molecules, a test vector is found in polynomial time i.e., O (nk). Its effectiveness in terms of Fault coverage, CPU time and Test vector generated is compared with some existing classical approaches like simulated annealing and genetic algorithms.