Adaptive Context-Sensitive Spelling Error Correction Techniques for the Extremely Unpredictable Error Generating Language Environments

The authors present a reformulation based architecture for threshold selection in adaptive foward error correction (FEC) decoding in wireless applications. The reformulation technique results in an efficient VLSI architecture with a significant reduction in hardware complexity. The paper describes the reformulation technique, its applications on the architecture for adaptive forward error correction (FEC) decoding algorithm and its implementations. We demonstrate that in addition to significant reduction in data path complexity, there is also a 25% to 47% storage reduction in the path metric unit (PMU).

Spell Correction is a widely referred problem in natural language processing. Since spelling errors prevent perfect transmission of the author's intended concepts to the audience, writers and researchers usually spend a lot of time reviewing, to detect and correct spelling errors in their writings. Therefore, an automatic tool would be a great help in this area. Persian language is very prone to these errors due to its unique features. Also, the introduction of Arabic sentences and terms into this language has increased the challenges in spelling correction. Thus, there is a need for a tool that can detect and correct spelling errors in bilingual Persian and Arabic content. In this work, a supervised deep learning-based approach is proposed which benefits from a conditional random field (CRF) recurrent neural network to correct bilingual Arabic and Persian spelling errors. In order to create a suitable data set for training and testing the model presented in the proposed approach, 220,000 sentences with Arabic and Persian content were taken. Then artificially and using the methods of producing correct and error pairs, spelling errors were generated. In the next step, using the neural network based on the conditional random field, a model was presented that takes the features extracted from the data set as input to the network and makes predictions. The design of these features is one of the important points in this type of implementation. The results of the evaluations show that the proposed approach has a good and acceptable accuracy as the first bilingual Arabic and Persian Spell Corrector.

This paper proposes novel approaches for checking and correcting spelling errors in Sinhala essays written by candidates of grade five scholarship examination. They don't have a proper mechanism to identify their spelling mistakes in essays by themselves. Spelling errors by such students may occur due to the violation of spelling rules, missing or adding of letters, missing modifiers, inaccurate spelling in a similar structure, and similar sound letters]. To mitigate such challenges, the Sinhala corpus file has been developed to identify the accurate and inaccurate spellings of the written words. The role of this application is to identify the correct and incorrect words which are entered by the user and generate the most correct words as suggestions for the incorrect words. This paper introduces three new novel approaches to detect the correctly spelled words in Sinhala essays namely object word checker method, suffixes checker method and similar word checker method. With addition to that this paper discusses three approaches to generate accurate suggestions including one novel approach. When evaluating the accuracy of the spelling error detection and correction module the overall results for precision, recall, and the f -measure were recorded as 83.05%, 85.57%, and 86.62% respectively.

We propose an unsupervised context-aware spelling error detection and correction method for traditional Bangla written script. The context-aware capability is introduced by generating a representation of information from the context words and using the concept of cosine similarity of find the best candidate for the misspelled words. We used fast-text embedding concept of using character n-gram embedding. The character n-gram embedding is capable of generating embedding for unknown words, which is a capability missing in any conventional methods. We performed our experiments on Bangla language collected from popular newspapers, blogs, Bangla Wikipedia and Banglapedia. Our proposed method outperforms conventional methods.

Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction

This paper introduces Adaptive and Reliable Forward Error Correction (AR_FEC), a novel mechanism that is designed to ensure reliable and efficient real-time video delivery for Unmanned Aerial Vehicles (UAVs). UAVs communicate using wireless channels that are inherently unreliable. AR_FEC is an application-layer solution that is inspired by edge computing. It incorporates several elements, specifically, adaptive Forward Error Correction (FEC), dynamic video quality, and Unequal Error Protection (UEP). AR_FEC dynamically adjusts the number of redundancy frames per video Group of Picture (GoP) based on packet loss rate, recent average round-trip times, and cross-layer information such as bandwidth usage and network traffic load. Moreover, AR_FEC reacts to congestion via a dynamic video quality algorithm. By adaptively determining suitable redundancy for the video I-frames and adjusting video quality, significant improvements are achieved. Comprehensive simulations demonstrate that the proposed AR_FEC protocol offers excellent performance for the number of delivered frames, latency, throughput, and delivery of frames, within the delay constraints for real-time multimedia streaming from UAVs.

Robust image steganography against lossy JPEG compression based on embedding domain selection and adaptive error correction

The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

In this paper we present a method for registering a pair of differently exposed Low Dynamic Range (LDR) images for the purpose of rendering a High Dynamic Range (HDR) image. In general, the images are captured from a moving camera and/or contain moving objects. Therefore, proper registration is required to enable HDR rendering. However, even for equally exposed images, registration is an ill posed problem where errors are expected for a wide range of image pairs. The problem only becomes more challenging for a pair of differently exposed images. We propose an adaptive registration error detection and correction method to address this issue. By combining Optical Flow with the proposed correction method, we achieve state-of-the art results as shown in numerous experiments. The proposed method is simple and has low-complexity, hence allowing for an easy and efficient implementation.

An End-to-End Method for Chinese Spelling Error Detection and Correction

A hybrid forecasting model of carbon emissions with optimized VMD and error correction

In this paper we present the Arib system for Arabic spelling error detection and correction as part of the second Shared Task on Automatic Arabic Error Correction. Our system contains many components that address various types of spelling error and applies a combination of approaches including rule based, statistical based, and lexicon based in a cascade fashion. We also employed two core models, namely a probabilistic-based model and a distance-based model. Our results on the development and test set indicate that using the correction components in cascaded way yields the best results. The overall recall of our system is 0.51, with a precision of 0.67 and an F1 score of 0.58.

In this paper, an encoder and decoder system is proposed using Bose-Chaudhuri-Hocquenghem (BCH) double-error-correcting and triple-error detecting (DEC-TED) with emerging memories of low power and high decoding efficiency. An adaptive error correction technique and an invalid transition inhibition technique is enforced to the decoder. This is to improve the decoding efficiency and reduce the power consumption and delay. The adaptive error correction gives high decoding efficiency and invalid transition technique reduce the power consumption issue in conventional BCH decoders. The DEC-TED BCH decoder combines these two techniques by using a specific Error Correcting Code Clock and Flip Flops. This technique provides an error correcting encoder and decoder solution for low power and high-performance application using emerging memories. The design simulated in Xilinx FPGA using ISE Design Suite 14.5.

Automatic speech recognition (ASR) systems often make unrecoverable errors due to subsystem pruning (acoustic, language and pronunciation models); for example, pruning words due to acoustics using short-term context, prior to rescoring with long-term context based on linguistics. In this work, we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those. The proposed system can exploit long-term context using a neural network language model and can better choose between existing ASR output possibilities as well as re-introduce previously pruned or unseen (Out-Of-Vocabulary) phrases. It provides corrections under poorly performing ASR conditions without degrading any accurate transcriptions; such corrections are greater on top of out-of-domain and mismatched data ASR. Our system consistently provides improvements over the baseline ASR, even when baseline is further optimized through Recurrent Neural Network (RNN) language model rescoring. This demonstrates that any ASR improvements can be exploited independently and that our proposed system can potentially still provide benefits on highly optimized ASR. Finally, we present an extensive analysis of the type of errors corrected by our system.

Studying phase error is central to synthetic aperture radar (SAR) research. Phase error due to the real motion status of the SAR platform and propagation effects can reduce the utility of high-resolution SAR images even with theoretical estimation-based phase error correction. Adaptive optics (AO) can be used to correct optical aberrations. This study proposes an advanced, high-resolution SAR optoelectronic processor by integrating conventional processors with sensor-less AO techniques. This processor provides accurate adaptive phase error correction capabilities. The processing results of SAR echo data demonstrate the effectiveness of the proposed system in adaptive phase error correction and imaging improvement.