Shamita Ghosh

Bio: Shamita Ghosh is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Standard test image & Optical character recognition. The author has an hindex of 1, co-authored 2 publications receiving 25 citations.

Composite Script Identification and Orientation Detection for Indian Text Images

Abstract: A major preprocessing step in a multi-script OCR is to identify the script type of the test document image. The published papers on script identification usually assume that the test image is in correct i.e. 0° orientation. But by mistake a document may be fed to the system in wrong orientation, say at an angle of nearly 180° or ±90°. In this method we propose a script identification method that works for unknown orientation for all 11 official Indian scripts. Here, we first find the skew and counter-rotate the document by the skew angle. This will lead to correct (0°) or upside down (180°) orientation. Then script identification is done by a multi-stage tree classifier using features invariant to 0°/180° orientation. Next we go to find the orientation of the image by a two class classifier for each script. Performance of the proposed method has been tested on a variety of documents and promising results have been obtained.

Orientation detection of major Indian scripts

Abstract: The problem of orientation detection of all major printed Indian scripts is considered. For a large skew, the corrected document may be oriented in 0° or 180°. The OCR system will make fatal error if the document is in 180° orientation. We tested with several simple features like gray density, vertical/horizontal crossing counts, border directions and water reservoir area to detect orientation and found that the last one works uniformly good for all 11 classes of printed Indian scripts. Experimental results are presented on moderate data sets of all scripts.

Script Identification of Multi-Script Documents: A Survey

Abstract: In recent years, with the widespread of Internet and digitized processing of multi-script documents worldwide, script identification techniques have become more important in the pattern recognition field. Script identification concerns methods for identifying different scripts in multi-lingual, multi-script documents. This paper presents a comprehensive overview on research activities in the field and focuses on the most valuable results obtained so far. The most vital processes in script identification are addressed in detail: identification and discriminating methods, features extraction (local and global), and classification. Different kinds of approaches have been developed and promising results have been achieved. This paper reports SoA performance results. This paper reports methods concerning handwritten, printed, and hybrid document processing. More research is necessary to meet the performance levels essential for everyday applications.

New Spatial-Gradient-Features for Video Script Identification

Abstract: In this paper, we present new features based on Spatial-Gradient-Features (SGF) at block level for identifying six video scripts namely, Arabic, Chinese, English, Japanese, Korean and Tamil This works helps in enhancing the capability of the current OCR on video text recognition by choosing an appropriate OCR engine when video contains multi-script frames The input for script identification is the text blocks obtained by our text frame classification method For each text block, we obtain horizontal and vertical gradient information to enhance the contrast of the text pixels We divide the horizontal gradient block into two equal parts as upper and lower at the centroid in the horizontal direction Histogram on the horizontal gradient values of the upper and the lower part is performed to select dominant text pixels In the same way, the method selects dominant pixels from the right and the left parts obtained by dividing the vertical gradient block vertically The method combines the horizontal and the vertical dominant pixels to obtain text components Skeleton concept is used to reduce pixel width to a single pixel to extract spatial features We extract four features based on proximity between end points, junction points, intersection points and pixels The method is evaluated on 770 frames of six scripts in terms of classification rate and is compared with an existing method We have achieved 821% average classification rate

Automatic Handwritten Indian Scripts Identification

Abstract: Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a word level handwritten Indian script identification technique. To handle this, words are first segmented by morphological dilation and performed connected component labelling. We then employ the Radon transform, discrete wavelet transform, statistical filters and discrete cosine transform to extract the directional multi-resolution spatial features. We tested the features by using linear discriminant analysis, support vector machine and K-nearest neighbour classifiers over 11 different major Indian scripts (including Roman) in bi-script and tri-script scenario. In our tests, we have achieved maximum accuracies of 98% and 96% for bi-script and tri-scipt respectively.

Gradient-Angular-Features for Word-wise Video Script Identification

Abstract: Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate.