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A System for Handwritten and Machine-Printed Text Separation in Bangla Document Images

A System for Handwritten and Machine-Printed Text Separation...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Purnendu Banerjee B.B. Chaudhuri Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

In this paper, we describe an approach to distinguish between hand-written text and machine-printed text from annotated machine-printed Bangla Documents images. In applications involving OCR, distinction of machine-printed and hand-written characters is important, so that they can be sent to separate recognition engines. Identification of hand-written parts is useful in deleting those parts and cleaning the document image as well. In this paper a classification system is presented which takes a connected component in the document image and assigns them to two classes namely "machine-printed" and for "hand-written" classes, respectively. The proposed system contains a preprocessing step, which smoothes the object border and finds the Connected Component. Bangla script specific features are extracted from that Connected Component image, and a standard classifier based on SVM generates the final response. Experimental results on a data set show that the proposed approach achieves an overall accuracy of 96.49%.

关键词： Support vector machines Feature extraction Handwriting recognition Accuracy Text recognition Training

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Segmentation of touching characters in printed Devnagari and Bangla scripts using fuzzy multifactorial analysis

Segmentation of touching characters in printed Devnagari and...

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International Conference on Document Analysis and recognition

作者： U. Garain B.B. Chaudhuri Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta India

ISBN: (纸本)0769512631

Existence of touching characters in scanned documents is a major problem in designing an effective character segmentation procedure for OCR systems. In this paper, new techniques are presented for identification and segmentation of touching characters. The techniques are based on fuzzy multifactorial analysis. A predictive algorithm is developed for effectively selecting cut-points to segment touching characters. Initially, our proposed method has been applied for segmenting touching characters that appear in Devnagari (Hindi) and Bangla, two major scripts in the indian sub-continent. The results obtained from a test-set of considerable size show that a high recognition rate can be achieved with a reasonable amount of computations.

关键词： Optical character recognition software Character recognition Decision making Fuzzy systems pattern analysis computer vision pattern recognition Prediction algorithms Testing Degradation

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A Two Stage recognition Scheme for Handwritten Tamil Characters

A Two Stage Recognition Scheme for Handwritten Tamil Charact...

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International Conference on Document Analysis and recognition

作者： U. Bhattacharya S.K. Ghosh S. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

India is a multilingual multiscript country with more than 18 languages and 10 different major scripts. Not enough research work towards recognition of handwritten characters of these indian scripts has been done. Tamil, an official as well as popular script of the southern part of India, Singapore, Malaysia, and Sri Lanka has a large character set which includes many compound characters. Only a few works towards handwriting recognition of this large character set has been reported in the literature. Recently, HP Labs India developed a database of handwritten Tamil characters. In the present paper, we describe an off-line recognition approach based on this database. The proposed method consists of two stages. In the first stage, we apply an unsupervised clustering method to create a smaller number of groups of handwritten Tamil character classes. In the second stage, we consider a supervised classification technique in each of these smaller groups for final recognition. The features considered in the two stages are different. The proposed two-stage recognition scheme provided acceptable classification accuracies on both the training and test sets of the present database.

关键词： Character recognition Handwriting recognition Spatial databases pattern recognition Testing Speech recognition computer vision Natural languages Clustering methods Personal communication networks

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Direction Code Based Features for recognition of Online Handwritten Characters of Bangla

Direction Code Based Features for Recognition of Online Hand...

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International Conference on Document Analysis and recognition

作者： U. Bhattacharya B.K. Gupta S. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In the present article, we describe a novel direction code based feature extraction approach for recognition of online Bangla handwritten basic characters. We have implemented the proposed approach on a database of 7043 online handwritten Bangla (a major script of the indian subcontinent) character samples, which has been developed by us. This is a 50-class recognition problem and we achieved 93.90% and 83.61% recognition accuracies respectively on its training and test sets.

关键词： Character recognition Handwriting recognition Spatial databases pattern recognition Histograms Shape Testing Personal digital assistants computer vision Feature extraction

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Devanagari and Bangla Text Extraction from Natural Scene Images

Devanagari and Bangla Text Extraction from Natural Scene Ima...

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International Conference on Document Analysis and recognition

作者： Ujjwal Bhattacharya Swapan Kumar Parui Srikanta Mondal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

With the increasing popularity of digital cameras attached with various handheld devices, many new computational challenges have gained significance. One such problem is extraction of texts from natural scene images captured by such devices. The extracted text can be sent to OCR or to a text-to-speech engine for recognition. In this article, we propose a novel and effective scheme based on analysis of connected components for extraction of Devanagari and Bangla texts from camera captured scene images. A common unique feature of these two scripts is the presence of headline and the proposed scheme uses mathematical morphology operations for their extraction. Additionally, we consider a few criteria for robust filtering of text components from such scene images. Moreover, we studied the problem of binarization of such scene images and observed that there are situations when repeated binarization by a well-known global thresholding approach is effective. We tested our algorithm on a repository of 100 scene images containing texts of Devanagari and / or Bangla.

关键词： Layout Handheld computers Digital cameras Optical character recognition software Speech synthesis Engines Text recognition Image analysis Morphology Robustness

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Segmentation of Bangla handwritten text into characters by recursive contour following

Segmentation of Bangla handwritten text into characters by r...

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International Conference on Document Analysis and recognition

作者： A. Bishnu B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Calcutta India

Segmentation of handwritten words into characters is one of the important components in handwritten text OCR. In this paper we put forward a method for the segmentation of handwritten Bangla (an Indo-Bangladeshi language) text into characters. Based on certain characteristics of Bangla writing methods, different zones across the height of the word are detected. These zones provide certain structural information about the constituent characters of the respective word. In Bangla handwritten texts often there is overlap between rectangular hulls of successive characters. As such the characters are seldom vertically separable. So, we propose a method of recursive contour following in one of the zones across the height of the word to find out the extents within which the main portion of the character lies. If the successive characters are not touching in the zone of contour following, the algorithm gives fairly good results.

关键词： Image segmentation Optical character recognition software Hidden Markov models pattern recognition Image analysis Lattices computer vision Writing Feature extraction Linear programming

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Aquaformer: Underwater Image Enhancement via Adaptive Transformer

Aquaformer: Underwater Image Enhancement via Adaptive Transf...

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International Joint Conference on Neural Networks (IJCNN)

作者： Harsh Bhandari Sarbani Palit Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Water causes degradation of quality in optical images captured underwater due to its physical properties of absorption and scattering. This degradation is further aggravated by the increase in water depth and the presence of contaminated water. Transformers in the vision domain have made a quantum leap in many vision tasks such as detection, and segmentation but yet to make any progress in enhancing degraded underwater images. We propose a transformer-based model named “Aquaformer” which makes four major contributions: an adaptive layer normalization, replacement of masked cyclic shift with symmetric padding in window partitioning, a novel aggregation mechanism, and an adjustable fusion approach. These succeed in making the model a very powerful one, producing significantly better performance compared to the latest state-of-the-art methods. Testing on multiple benchmark datasets, employing both quantitative and qualitative metrics, establishes its supremacy.

关键词： Degradation Water Adaptation models Scattering Benchmark testing Transformers Water pollution

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Bag-of-features HMMs for segmentation-free Bangla word spotting 13

Bag-of-features HMMs for segmentation-free Bangla word spott...

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Rothacker, L. Fink, G.A. Banerjee, P. Bhattacharya, U. Chaudhuri, B.B. Department of Computer Science TU Dortmund University 44221 Dortmund Germany Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-700 0108 India

ISBN: (纸本)9781450321143

In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our evaluation we are able to report high retrieval scores on a new printed Bangla dataset. Furthermore, we outperform state-of-the-art results on the well-known George Washington word spotting benchmark. Both results have been achieved using an almost identical parametric method configuration. © 2013 ACM.

关键词： Hidden Markov models

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A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

A Machine Learning Approach to Detection of Core Region of O...

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International Workshop on Frontiers in Handwriting recognition

作者： S. Baral S. Bhattacharya A. Chakraborty U. Bhattacharya S.K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. Are often based on this core region. This is particularly useful for word recognition of major indian scripts, which have large character sets. The main parts of majority of these characters belong to the core region that is bounded above by a headline and bounded below by an imaginary base line. Only a few such characters or their parts appear either above or below the core region. A few approaches are available in the literature for detection of such a core region of offline handwritten word samples of Latin script. Also, a similar region is often determined for recognition of images of printed indian scripts. However, none of these approaches have studied detection of core region of an unconstrained online handwritten word. In this article, we propose a novel method for detection of the core region of online handwritten word samples of Bangla, a major indian script. For this we first perform smoothing on the samples and then segment a stroke into sub strokes. We compute certain novel positional features from each such sub stroke. Using these features, a multilayer perceptron (MLP) is trained by back propagation (BP) algorithm. On the basis of the output of the MLP, we determine the position of both the headline and the baseline. We have tested this approach on a recently developed large database of online unconstrained handwriting Bangla word samples. The proposed approach would also work on similar samples of Devanagari, another major indian script. Experimental results are encouraging.

关键词： Vectors Training Feature extraction Shape Databases Compounds Histograms

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Neural network based word-wise handwritten script identification system for indian postal automation

Neural network based word-wise handwritten script identifica...

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International Conference on Intelligent Sensing and Information Processing (ICISIP)

作者： K. Roy U. Pal B.B. Chaudhuri Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piecewise projection method the destination address block (DAB), is at first segmented into lines and then into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, fractal based feature, etc. a neural network (NN) classifier is generated for word-wise Bangla and English scripts identification. Overall accuracy of the proposed system is at present 9 7.62%.

关键词： Neural networks Automation Natural languages Water resources Reservoirs Fractals Optical character recognition software computer vision pattern recognition USA Councils

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