The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20, 000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.
Abstract. Handwritten essays are widely used in educational assessments, particularly in classroom instruction. This paper concerns the design of an automated system for performing the task of taking as input scanned images of handwritten student essays in reading comprehension tests and to produce as output scores for the answers which are analogous to those provided by human scorers. The system is based on integrating the two technologies of optical handwriting recognition (OHR) and automated essay scoring (AES). The OHR system performs several pre-processing steps such as forms removal, rule-line removal and segmentation of text lines and words. The final recognition step, which is tuned to the task of reading comprehension evaluation in a primary education setting, is performed using a lexicon derived from the passage to be read. The AES system is based on the approach of latent semantic analysis where a set of human-scored answers are used to determine scoring system parameters using a machine learning approach. System performance is compared to scoring done by human raters. Testing on a small set of handwritten answers indicate that system performance is comparable to that of automatic scoring based on manual transcription.
Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of deep learning techniques. We use a joint deep neural network concept it consist of two sub models. The first model is responsible for hiding digital audio into a digital image. The second model is responsible for returning a digital audio from the stego image. Various vast experiments are conducted with a set of 24K images and also for various sizes of images. From the experiments it can be seen proposed method is performing more effective than the existing methods. The proposed method also concentrates the integrity of the digital image and audio files.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.