Enhancing Predictability of Handwritten Document Content using HTR and Word Substitution

Handwritten Text Recognition (HTR) can become progressively abysmal when the documents are damaged with smudges, blemishes and blurs. Recognition of such documents is a challenging task. We, therefore propose a system to identify textual handwritten content in documents where the state-of-the-art Optical Character Recognition (OCR) existing at its full extent performs with low accuracy. By introducing word substitution using character and distance analysis for spell checking and word completion in such areas for giving out more accurate results using a word corpus, we improved our prediction results especially in cases where the OCR is prone to predict false positives on the smudge areas predominantly. Blur detection on every word before segmentation is also substituted with a new word by our OCR algorithm to avoid false positive results and are instead substituted with suitable words. This methodology is far more convenient and reliable since even state-of-the-art HTR technologies do not have more than 71% accuracy. The accuracy of the predicted test is measured using the text similarity metric - Fuzzy Token Set Ratio (FTSR)

An investigative design of optimum stochastic language model for bangla autocomplete

<p class="Abstract">Word completion and word prediction are two important phenomena in typing that have extreme effect on aiding disable people and students while using keyboard or other similar devices. Such autocomplete technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. <em>N</em>-gram based language models are proposed for autocomplete a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.</p>

Violent Lyrics = Aggressive Listeners?

Research on music has had an impressive impact. For example, the semantic content of lyrics seems to cause associated short-term effects regarding cognition and affect. However, we argue that these effects might have been confounded by other musical parameters related to time, pitch, texture, or voice of the selected songs. This study overcame this methodological problem by using different versions of an experimentally manipulated song. In a 2 × 2 between-subjects design, 120 university students listened to four versions of a song with violent or prosocial lyrics presented in slow or fast tempo. As predicted by theories of priming, violent lyrics increased aggressive cognitions (word completion test) and aggressive affect (self-reported state anger) in comparison with prosocial lyrics. However, the reverse effects of prosocial lyrics on prosocial cognitions and prosocial affect could not be confirmed. Finally, the tempo of the song did not consistently increase self-reported arousal, and we did not find more extreme effects under conditions of fast tempo as predicted by the arousal-extremity model.