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作者： Garain, U. Chaudhuri, B.B. Pal, T.T. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata 700 108 India

This paper presents the online handwriting recognition for indian scripts. The primary concern of the approach is the modeling of human motor functionality while writing characters. This is achieved by looking at the whole pen trajectory where the time evaluation of the pen coordinates plays a crucial role. A low complexity classifier has been designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles. Initially, the approach has been applied for online recognition of handwritten characters in Devnagari and Bangla, the two major indian scripts. A test on a dataset of considerable size shows promising recognition rates namely, 97.29% for Devnagari and 96.34% for Bangla. © 2002 IEEE.

关键词： pattern recognition

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recognition of handprinted bangla numerals using neural network models 5th

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5th International Conference on Asian Fuzzy Systems Society, AFSS 2002

作者： Bhattacharya, U. Das, T.K. Datta, A. Parui, S.K. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta35 India Computer and Statistical Service Center Indian Statistical Institute 203 B. T. Road Calcutta35 India

ISBN: (纸本)9783540431503

This paper proposes an automatic recognition scheme for hand printed Bangla (an indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Neural network models

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Word–wise script identification from indian documents

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6th IAPR International Workshop on Document Analysis Systems, DAS 2004

作者： Sinha, Suranjit Pal, Umapada Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Unit 203 B.T. Road Kolkata700 108 India

ISBN: (纸本)3540230602

In a country like India, a single text line of most of the official documents contains two different script words. Under two-language formula, the indian documents are written in English and the state official language. For Optical Character recognition (OCR) of such a document page, it is necessary to separate different script words before feeding them to the OCRs of individual scripts. In this paper a robust technique is proposed to extract word-wise script identification from indian doublet form documents. Here, at first, the document is segmented into lines and then the lines are segmented into words. Using different topological and structural features (like number of loops, headline feature, water reservoir concept based features, profile features, etc.) individual script words are identified from the documents. The proposed scheme is tested on 24210 words of different doublets and we received more than 97% accuracy, on average. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Optical character recognition

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A new method for handwritten scene text detection in video

A new method for handwritten scene text detection in video

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International Conference on Frontiers in Handwriting recognition

作者： Shivakumara, Palaiahnakote Dutta, Anjan Pal, Umapada Tan, Chew Lim School of Computing National University of Singapore Singapore Singapore Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9780769542218

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K=2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising. © 2010 IEEE.

关键词： Color

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Text line identification in Tagore's manuscript

Text line identification in Tagore's manuscript

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2014 3rd IEEE Students' Technology Symposium, IEEE TechSym 2014

作者： Adak, Chandranath Chaudhuri, Bidyut B. Dept. of Computer Science and Engg. University of Kalyani West-Bengal-741235 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9781479926084

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. © 2014 IEEE.

关键词： document image processing handwritten character recognition image segmentation optical character recognition text analysis Rabindranath Tagore manuscript black run-length smearing connected component analysis document image cleaning doodle separation handwritten documents horizontal histogram nonuniform line preprocessing stage printed document text line identification method text line segmentation textual region uniform line straightness variable interline gaps window examination Character recognition Frequency modulation Handwriting recognition Histograms Image analysis Optical filters Text analysis document image analysis doodle handwritten document manuscript processing text line identification

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Bangla handwritten character recognition

Bangla handwritten character recognition

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2nd indian International Conference on Artificial Intelligence, IICAI 2005

作者： Roy, K. Pal, U. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Faculty of Engineering Mie University 1577 Kurimamachiya-cho Mie 514-8504 Japan

ISBN: (纸本)0972741216

recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten characters using quadratic classifier based on the features obtained from directional chain code histogram. Here the bounding box of a character is segmented into blocks and chain code histogram is computed in each of the blocks. This chain code features are fed to the quadratic classifier for recognition. We tested the result on 14879 characters and we obtained 90.53% accuracy with 0.81% rejection and 8.66% error, when 50 classes are considered. From 35 classes we obtained 93.90% accuracy with 0.91% rejection and 5.19% error rate. Copyright © IICAI 2005.

关键词： Graphic methods

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Handwritten bangla compound character recognition using gradient feature

Handwritten bangla compound character recognition using grad...

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10th International Conference on Information Technology, ICIT 2007

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University 1577 Kurimamachiya-cho Tsu Mie Japan

ISBN: (纸本)0769530680

recognition of handwritten characters of indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten compound characters using Modified Quadratic Discriminant Function (MQDF). The features used for recognition purpose are mainly based on directional information obtained from the arc tangent of the gradient. To get the feature, at first, a 2 X 2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the image. A Roberts filter is then applied on the normalized image to obtain gradient image. Next, the arc tangent of the gradient (direction of gradient) is initially quantized into 32 directions and the strength of the gradient is accumulated with each of the quantized direction. Finally, the frequencies of these directions are down sampled using Gaussian filter to get 392 dimensional feature vectors. Using 5-fold cross validation technique we obtained 85.90% accuracy from a dataset of Bangla compound characters containing 20,543 samples. © 2007 IEEE.

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A new method for arbitrarily-oriented text detection in video

A new method for arbitrarily-oriented text detection in vide...

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Sharma, Nabin Shivakumara, Palaiahnakote Pal, Umapada Blumenstein, Michael Tan, Chew Lim Griffith University QLD Australia School of Computing National University of Singapore Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

ISBN: (纸本)9780769546612

Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure. © 2012 IEEE.

关键词： Pixels

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Two-stage recognition of handwritten bangla alphanumeric characters using neural classifiers

Two-stage recognition of handwritten bangla alphanumeric cha...

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2nd indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Parui, S.K. Sridhar, M. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Electrical and Computer Engineering University of Michigan-Dearborn United States Faculty of Engineering Mie University Japan

ISBN: (纸本)0972741216

No significant research work towards recognition of handwritten Bangla characters has yet been done. Only a few works in this area are found in the literature which are based on small databases col-lected in laboratory environments. Recently, a large and representative database of Bangla character images [1] has been developed. The first part of this article is devoted to shape analysis of samples of this database vis-a-vis shapes of the respective printed characters. The next part describes a two-stage scheme for recognition of handwritten Bangla alphanumeric characters. This is a 57 class problem (shapes of 3 numerals are not distinguishable from shapes of 3 basic characters among 10 numerals and 50 basic characters). In the first stage of this recognition scheme, an input character is identified in one of 11 smaller groups of characters. In the second stage, classification is performed within the particular smaller group as determined by the first stage. In both the stages of the proposed approach, local chain code histograms of character skeleton are used as the feature vector and multilayer perceptron (MLP) as the classifier. recognition accuracies obtained by the proposed approach on the training and test sets of the present handwritten Bangla alphanumeric character database are respectively 91.45% and 84.65%. Copyright © IICAI 2005.

关键词： Database systems

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ANN based traffic congestion analysis applied to parking recommendation system for electric three-wheelers

Engineering Research Express

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Engineering Research Express 2025年第2期7卷

作者： Haldar, Suman Mondal, Arindam Maity, Rajkumar Banerjee, Rajib Department of Electronics and Communication Engineering Neotia Institute of Technology Management and Science West Bengal 743368 India Department of Electrical Engineering Dr B.C Roy Engineering College West Bengal Durgapur 713206 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal 700108 India School of Computer Science UPES Uttarakhand Dehradun 248007 India

The increasing adoption of Electric Vehicles (EVs) is transforming transportation systems with an emphasis on sustainability. In the Asia-Pacific region, Electric Three Wheelers (E3W) have gained significant market share due to their affordability and eco-friendly nature. However, the rise in E3W usage has also led to road congestion and parking challenges, particularly in suburban and rural areas. This paper presents a smart roadside parking recommendation system that utilizes a proposed Artificial Neural Network (ANN) to forecast traffic status and recommend nearby parking options. The system uses vehicle dynamics data such as voltage, current, and timestamps collected via an IoT-based data logger integrated with a cloud-based and map-based visualization interface. By analyzing the collected data, the system predicts real-time traffic status for parking recommendations, enabling an efficient parking solution that saves both time and energy. The experimental result demonstrates that the proposed ANN configuration gives the most optimal result in terms of accuracy, loss, MAE, MSE, RMSE, and R Square on purely unseen data in real-time scenarios. Moreover, the trained ANN model effectively categorizes the parking recommendation as ‘Recommended,’ ‘Waiting State,’ or ‘Not Recommended’ for specific parking areas. To validate the model performance, experiments are conducted in two distinct regions with unique routes and traffic patterns. The result is visualized through map API and demonstrates optimal parking recommendations as compared to real-world traffic scenarios, thereby improving decision-making for E3W drivers. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： artificial neural network battery monitoring data logger deep learning electric three-wheeler internet of things

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