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Automatic Detection of Handwritten Texts from Video Frames of Lectures

Automatic Detection of Handwritten Texts from Video Frames o...

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International Workshop on Frontiers in Handwriting recognition

作者： Purnendu Banerjee Ujjwal Bhattacharya Bidyut B. Chaudhuri Society for Natural Language Technology Research Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Automatic recognition of handwritten texts in video lectures has important applications. In video lectures, the presenter usually writes on white / colored board. The video camera often captures the writing board along with certain other objects possibly including the presenter itself. recognition of handwritten texts from such a video frame requires prior detection of the region of texts in the frame. In this article, we present our recent study of text localization in such video lecture frames. Here, we use Scale Invariant Feature Transform (SIFT) descriptors densely over the entire region of the frame. The descriptors are located on a regular grid of 5 pixels following the usual practice and considered a uniform patch size of 60 × 60 pixels as its support on the basis of an empirical study. This SIFT descriptor at each location (grid point) is fed as a 128-dimensional input feature vector to a Multilayer Perceptron (MLP) network which gives response for each grid point as either text or non-text. Depending on certain aggregate response at each pixel we localize text regions in the input video frame. Next, we employ K-means clustering to detect the text components present in the localized region of the video frame. Finally, two simple rules are applied to decide certain possible detected text components as noise. We obtained encouraging simulation results of this approach on a variety of video lecture frames.

关键词： Handwriting recognition Text recognition Training Databases Image color analysis Cameras Noise

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An Approach of Strike-Through Text Identification from Handwritten Documents

An Approach of Strike-Through Text Identification from Handw...

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International Workshop on Frontiers in Handwriting recognition

作者： Chandranath Adak Bidyut B. Chaudhuri Dept. of Computer Science & Engg University of Kalyani West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

A handwritten document may contain strike-through texts. If such texts are fed into an OCR system, the output will be garbage. In this paper, we propose a scheme to detect such strike-through texts/words. Using a graph based model, we represent a textual connected component as a graph. The start/end and intersection points of the ink-strokes of a component are marked as graph nodes. There exists an edge between two nodes if they are connected by object (ink) pixels. By eliminating parallel edges and self loops we obtain a simple, undirected, edge-weighted graph of the text-component. The edge-weight is found by adding horizontal/vertical moves weighted by 1 and diagonal moves weighted by √2. In this graph, we find the shortest path which is nearly as long as the width of the text component and maintains a reasonable degree of straightness. This path, if exist, is identified as the strike-through line. Here we deal with handwritten documents in English, Bengali and Devanagari script. Our approach delivers fairly good results.

关键词： Handwriting recognition Hidden Markov models Character recognition Optical character recognition software Image edge detection Ink Databases

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A Two Stage Approach for Handwritten Malayalam Character recognition

A Two Stage Approach for Handwritten Malayalam Character Rec...

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International Workshop on Frontiers in Handwriting recognition

作者： Jomy John K.V. Pramod Kannan Balakrishnan Bidyut B. Chaudhuri Dept. of Computer Applications Cochin University of Science and Technology Kochi India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Handwritten character recognition is still a research challenge in OCR discipline, especially for Indian scripts. This paper deals with handwritten Malayalam, a major Indian script, where past work considered a small subset of characters only. In this paper we deal with complete set of basic characters, vowel and consonant signs and compound characters that may be present in the script. Here the recognition task is more difficult because of large numbers of classes and high interclass similarity. To tackle the problem, we proposed a two-stage approach. The first stage is a group classifier, where a group consists of similar characters and those that misclassify among themselves. In the second stage, a character assigned to a group in the first stage is classified to a particular character class. The proposed scheme is more accurate and efficient than a single stage scheme.

关键词： Compounds Character recognition Support vector machines Shape Handwriting recognition Feature extraction Databases

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Semi-supervised Online Bayesian Network Learner for Handwritten Characters recognition

Semi-supervised Online Bayesian Network Learner for Handwrit...

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International Conference on pattern recognition

作者： Rituraj Kunwar Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479952106

This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset. The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.

关键词： Accuracy Bayes methods Training Equations Niobium Learning systems Estimation

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Text line identification in Tagore's manuscript

Text line identification in Tagore's manuscript

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IEEE Students' Technology Symposium (TechSym)

作者： Chandranath Adak Bidyut B. Chaudhuri Dept. of Computer Science & Engg. University of Kalyani West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a text line identification method is proposed. The text lines of printed document are easy to segment due to uniform straightness of the lines and sufficient gap between the lines. But in handwritten documents, the line is nonuniform and interline gaps are variable. We take Rabindranath Tagore's manuscript as it is one of the most difficult manuscripts that contain doodles. Our method consists of a preprocessing stage to clean the document image. Then we separate doodles from the manuscript to get the textual region. After that we identify the text lines on the manuscript. For text line identification, we use window examination, black run-length smearing, horizontal histogram and connected component analysis.

关键词： Handwriting recognition Histograms Image analysis Text analysis Optical filters Frequency modulation Character recognition

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Flexible Sequence Matching Technique: Application to Word Spotting in Degraded Documents

Flexible Sequence Matching Technique: Application to Word Sp...

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International Workshop on Frontiers in Handwriting recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratoire d'Informatique Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a new sequence-matching algorithm, called as Flexible Sequence Matching (FSM) algorithm is proposed. FSM combines several abilities of other sequence matching algorithms (especially DTW, CDP and MVM) that could be configured depending on the application domain. Its generality and robustness comes from its ability to find sub sequences (as in CDP), to skip outliers inside the match sequences (as in MVM) and to match multiple elements with a single one (as in CDP and DTW). These properties make it extremely suitable for robust word spotting. More precisely, the FSM algorithm has the capability to retrieve a query inside a line or piece of line. This facility is useful as word segmentation process may not work accurately or when only line segmentation information is available. Furthermore, thanks to its skipping capability, that makes the proposed FSM algorithm less sensible to local variations in the spelling of words, and also to local degradation effects. Finally, its multiple matching facilities (many to one and one to many matching) are useful in case of different length of target and query sequences due to the variability in scale factor. We demonstrate the superiority of proposed FSM algorithm in specific cases such as incorrect word segmentation and word level local variations. When different experiments were performed using handwritten George Washington dataset and also on historical typewritten document images, quite promising results were obtained.

关键词： Feature extraction Algorithm design and analysis Image segmentation computer architecture Robustness pattern recognition Dynamic programming

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ICFHR 2014 Competition on recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)

ICFHR 2014 Competition on Recognition of On-Line Handwritten...

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International Workshop on Frontiers in Handwriting recognition

作者： H. Mouchère C. Viard-Gaudin R. Zanibbi U. Garain IRCCyN/IVC LUNAM Université de Nantes Department of Computer Science Rochester Institute of Technology Rochester NY USA Computer Vision and Pattern Recognition (CVPR) Indian Statistical Institute

We present the outcome of the latest edition of the CROHME competition, dedicated to on-line handwritten mathematical expression recognition. In addition to the standard full expression recognition task from previous competitions, CROHME 2014 features two new tasks. The first is dedicated to isolated symbol recognition including a reject option for invalid symbol hypotheses, and the second concerns recognizing expressions that contain matrices. System performance is improving relative to previous competitions. Data and evaluation tools used for the competition are publicly available.

关键词： Feature extraction Training Handwriting recognition Grammar Mathematical model Layout Ink

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Stroke Level User-Adaptation for Stroke Order Free Online Handwriting recognition

Stroke Level User-Adaptation for Stroke Order Free Online Ha...

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International Workshop on Frontiers in Handwriting recognition

作者： D. Dutta A. Roy Chowdhury U. Bhattacharya S.K. Parui Dept. of Information Technology Heritage Institute of Technology Kolkata India Heritage Institute of Technology Kolkata West Bengal IN Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, a novel lightweight user-adaptive online handwriting recognition scheme has been presented. The present recognition approach is stroke order free. It is based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database. In this approach a very small number of prototypes of each stroke shape is used along with certain weighted DTW distance based nearest neighbour classifier to recognize the strokes in the input character. Individual characters are identified using a look-up table (LUT) each row of which corresponds to one character composed of a distinct set of stroke shapes. This LUT is formed using the representative sample set of the underlying character set. If a stroke does not find a close match in the training set or if the set of strokes for an input character does not find a corresponding entry in the LUT, user adaptation takes place using a modified Learning Vector Quantization (LVQ) method. The proposed scheme has been implemented in an Android-based Bangla handwriting recognizer [1] for handheld devices and its recognition performance is encouraging.

关键词： Handwriting recognition Character recognition Libraries Prototypes Table lookup Accuracy Writing

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Design of Unsupervised Feature Extraction System for On-line Bangla Handwriting recognition

Design of Unsupervised Feature Extraction System for On-line...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Volkmar Frinken Nilanjana Bhattacharya Umapada Pal Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka-shi Japan Bose Institute Kolkatta India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkatta India

Different systems for handwriting recognition use different features to represent the input text. Even after decades of research, no favorable decision on a best-practice exists and many features are carefully hand-crafted. To facilitate the design phase for on-line handwriting systems, in this paper, we propose an unsupervised feature generation approach based on dissimilarity space embedding (DSE) of local neighborhoods around the points along the trajectory. DSE has high capability of discriminative representation and hence beneficial for classification. We compare the approach with a state-of-the-art feature extraction method and demonstrate its superiority.

关键词： Feature extraction Prototypes Handwriting recognition Writing Hidden Markov models Character recognition Text recognition

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Keyword spotting for self-training of BLSTM NN based handwriting recognition systems

Keyword spotting for self-training of BLSTM NN based handwri...

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作者： Frinken, Volkmar Fischer, Andreas Baumgartner, Markus Bunke, Horst Computer Vision Center Autonomous University of Barcelona Edifici O 08193 Bellaterra Barcelona Spain Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 CH-3012 Bern Switzerland

The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. © 2013 Elsevier Ltd. All rights reserved.

关键词： Character recognition

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