Preserving the Linguistic Diversity of Uttarakhand: Role of Language and Education Policies

The People’s Linguistic Survey of India has listed at least 13 languages from Uttarakhand, none of which are a part of Indian Constitution’s Eight Schedule. However, two of them (Kumaoni and Garhwali) are a part of UNESCO’s list of endangered languages. Garhwali is spoken by 23 lakh people in Uttarkahnd, while Kumaoni is the native language of about 20 lakh people. More than 40% of the state's population communicate using native languages and yet Hindi is the only official language of Uttarakhand. This research article seeks to examine the language and educational policies at both state and national level, their goals, implementation, and effectiveness in supporting the regional languages of Uttarakhand

Character Segmentation for Telugu Image Document using Multiple Histogram Projections

TEXT line segmentation is one of the major component of document image analysis. Text line segmentation is necessary to detect all text regions in the document image. In this paper we propose an algorithm based on multiple histogram projections using morphological operators to extract features of the image. Horizontal projection is performed on the text image, and then line segments are identified by the peaks in the horizontal projection. Threshold is applied to divide the text image into segments. False lines are eliminated using another threshold. Vertical histogram projections are used for the line segments and decomposed into words using threshold and further decomposed to characters. This approach provides best performance based on the experimental results such as Detection rate DR (98%) and Recognition Accuracy RA (98%)

Deep Learning-based Recognition of Devanagari Handwritten Characters

Numerous techniques have been used over many years to study handwriting recognition. There are two methods for reading handwriting, one of which is online and the other offline. Image recognition is the main part of the handwriting recognition process. Image recognition gives careful consideration to the picture's dimensions, viewing angle, and image quality. Machine learning and deep learning techniques are the two areas of focus for developers looking to increase the intelligence of computers. A person may learn to perform a task by repeatedly exercising it until they recall how to do it. His brain's neurons begin to work automatically, enabling him to carry out the task he has quickly learned. This and deep learning are fairly similar. It uses a variety of neural network designs to address a range of problems. The convolution neural network (CNN) is a very effective technique for handwriting and picture detection

Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System

Character recognition system based on human inspection is unpractical due to lack of accuracy and high cost. Therefore, investigating on automated character inspection system by computer is needed to improve the accuracy, reduce the cost and inspection time. In this project, a Beagle Bone Black (BBB) was used as a processing device and Logitech webcam was used for as an image acquisition device. Total of 1080 training samples will undergo the image pre-processing, character segmentation, feature extraction and training using random forest classifier. The optimal parameter values of random forest classifier are determined by computing crossvalidation misclassification rate. The maximum number of splits, number of trees, and learning rate that yields the zeromisclassification rate is 1, 39 and 0.10 respectively. The process of testing random forest classifier was done using SN74LS27N chip under five different illuminations: no LED, one LED, two LED, three LED and four LED. From the experiments, it shows that the proposed system able to achieve 90.00% of accuracy within 1second to recognize characters on the SN74LS27N chip compared to 65.56% accuracy of human inspection

Handwritten Devanagari Text Recognition using Single Classifier Approach with VSPCA Scheme

In this research paper we used individual classifier approach for Handwritten Devanagari text recognition. We experimented different categorical classifiers namely   Random Forest Classifier (RFC), Support Vector Machine (SVM), K Nearest Neighbor Classifier (KNN), Logistic Regression Classifier (LogRegr), Decision Tree Classifier (DTree). Seven different feature sets are used namely Eccentricity, Euler Number, Horizontal Histogram, Vertical Histogram, HOG Features, LBP Features, and Statistical Features. The experimentation is carried out on 9434 different characters whose features are extracted from 220 handwritten image documents from PHDIndic_11 dataset. We deduced and implemented a unique scheme namely VSPCA scheme. VSPCA is Vectorization, Scaling, and Principal Component Analysis carried out on all feature sets before being given for model training. We obtained varied accuracies using all these five classifiers on all these six feature sets in which 99.52% highest accuracy is observed

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Estimation de l'inclinaison d'un document arabe manuscrit numérisé par analyse temps-fréquence des histogrammes de projection

International audienceNous présentons dans cet article une nouvelle méthode de détermination de l'inclinaison d'un document manuscrit arabe à l'aide d'une représentation temps-fréquence énergétique de la classe de Cohen. Cette méthode consiste à calculer d'abord les histogrammes de projection obtenus pour différents angles, puis à déterminer la valeur maximale de la représentation temps-fréquence de la racine carrée de ces histogrammes. L'orientation du document est alors estimée par l'angle de projection fournissant la valeur maximale la plus élevée. La méthode proposée a été testée sur 864 documents inclinés avec 9 représentations temps-fréquence différentes. Les résultats sont présentés et analysés à la fin de cet article