Classification of Test Documents Based on Handwritten Student ID's Characteristics

AbstractThe bag of words (BoW) model is an efficient image representation technique for image categorization and annotation tasks. Building good feature vocabularies from automatically extracted image feature vectors produces discriminative feature words, which can improve the accuracy of image categorization tasks. In this paper we use feature vocabularies based biometric characteristic for identification on student ID and classification of students’ papers and various exam documents used at the University of Mostar. We demonstrated an experiment in which we used OpenCV as an image processing tool and tool for feature extraction. As regards to classification method, we used Neural Network for Recognition of Handwritten Digits (student ID). We tested out proposed method on MNIST test database and achieved recognition rate of 94,76% accuracy. The model is tested on digits which are extracted from the handwritten student exams and the accuracy of 82% is achieved (92% correctly classified digits)

An IoT System for Converting Handwritten Text to Editable Format via Gesture Recognition

Evaluation of traditional classroom has led to electronic classroom i.e. e-learning. Growth of traditional classroom doesn’t stop at e-learning or distance learning. Next step to electronic classroom is a smart classroom. Most popular features of electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Numerous techniques have been implemented in order to extract handwriting from video/photo of the lecture but still the deficiency of few techniques can be resolved, and which can turn electronic classroom into smart classroom. In this thesis, we present a real-time IoT system to convert handwritten text into editable format by implementing hand gesture recognition (HGR) with Raspberry Pi and camera. Hand Gesture Recognition (HGR) is built using edge detection algorithm and HGR is used in this system to reduce computational complexity of previous systems i.e. removal of redundant images and lecture’s body from image, recollecting text from previous images to fill area from where lecture’s body has been removed. Raspberry Pi is used to retrieve, perceive HGR and to build a smart classroom based on IoT. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, graphs and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT, the editable format of lecture notes is given to students via desktop application which helps students to edit notes and images according to their necessity

Recognition-based Approach of Numeral Extraction in Handwritten Chemistry Documents using Contextual Knowledge

International audienceThis paper presents a complete procedure that uses contextual and syntactic information to identify and recognize amount fields in the table regions of chemistry documents. The proposed method is composed of two main modules. Firstly, a structural analysis based on connected component (CC) dimensions and positions identifies some special symbols and clusters other CCs into three groups: fragment of characters, isolated characters or connected characters. Then, a specific processing is performed on each group of CCs. The fragment of characters are merged with the nearest character or string using geometric relationship based rules. The characters are sent to a recognition module to identify the numeral components. For the connected characters, the final decision on the string nature (numeric or non-numeric) is made based on a global score computed on the full string using the height regularity property and the recognition probabilities of its segmented fragments. Finally, a simple syntactic verification at table row level is conducted in order to correct eventual errors. The experimental tests are carried out on real-world chemistry documents provided by our industrial partner eNovalys. The obtained results show the effectiveness of the proposed system in extracting amount fields

Adding feedback to improve segmentation and recognition of handwritten numerals

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Susan A. Dey.S.B.and M.Eng

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Text Recognition Past, Present and Future

Text recognition in various images is a research domain which attempts to develop a computer programs with a feature to read the text from images by the computer. Thus there is a need of character recognition mechanisms which results Document Image Analysis (DIA) which changes different documents in paper format computer generated electronic format. In this paper we have read and analyzed various methods for text recognition from different types of text images like scene images, text images, born digital images and text from videos. Text Recognition is an easy task for people who can read, but to make a computer that does character recognition is highly difficult task. The reasons behind this might be variability, abstraction and absence of various hard-and-fast rules that locate the appearance of a visual character in various text images. Therefore rules that is to be applied need to be very heuristically deduced from samples domain. This paper gives a review for various existing methods. The objective of this paper is to give a summary on well-known methods

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. Our BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result it can adaptively determine the step size for current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. We prove that, by repeating such branch-and-pruning procedure, we can locate the global optimality within finite iterations. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation