Purnendu Banerjee

An approach for Bangla and Devanagari video text recognition

Extraction and recognition of Bangla text from video frame images is challenging due to fonts type and style variation, complex color background, low-resolution, low contrast etc. In this paper, we propose an algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background. Here, a two-step approach has been proposed. After text localization, the text line is segmented into words using information based on line contours. First order gradient values of the text blocks are used to find the word gap. Next, an Adaptive SIS binarization technique is applied on each word. Next this binarized text block is sent to a state of the art OCR for recognition.

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

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.

Automatic Detection of Handwritten Texts from Video Frames of Lectures

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.

Gabor filter based hand-drawn underline removal in printed documents

In this paper, a novel technique has been used for underline detection and removal in a Bengali and English document using Gabor filter and connected component analysis. In the underline detection module we first use Gabor filter in a specific direction to detect underline region and then use connected component analysis to detect particular underline. In second part i.e. underline removal module we use nearest neighbor approach. Our algorithm used for both touched and untouched underline. The experimental result of our proposed method demonstrates that it is able to improve Optical Character recognition (OCR) recognition accuracy.

Video text localization using wavelet and shearlet transforms

Text in video is useful and important in indexing and retrieving the video documents efficiently and accurately. In this paper, we present a new method of text detection using a combined dictionary consisting of wavelets and a recently introduced transform called shearlets. Wavelets provide optimally sparse expansion for point-like structures and shearlets provide optimally sparse expansions for curve-like structures. By combining these two features we have computed a high frequency sub-band to brighten the text part. Then K-means clustering is used for obtaining text pixels from the Standard Deviation (SD) of combined coefficient of wavelets and shearlets as well as the union of wavelets and shearlets features. Text parts are obtained by grouping neighboring regions based on geometric properties of the classified output frame of unsupervised K-means classification. The proposed method tested on a standard as well as newly collected database shows to be superior to some of the existing methods.

Automatic Hyperlinking of Engineering Drawing Documents

In construction or manufacturing industry, engineering drawings are used as blueprint or plan documents to facilitate the construction or manufacturing process. A fairly large construction project involves very large number of these documents, divided into different sub-sections. An engineer or architect often needs to refer different documents while preparing a new one or marking some irregularity in some document. Therefore they need to navigate through different files. It becomes an extremely difficult and time consuming task to move from one file to another in an interactive way. This paper describes an automated technique to access information from the existing drawing documents and create hyperlinks in order to enable the engineers to quickly navigate between files. The overall accuracy of our system for a class of documents is a decent 94.46%.