Whether language change is abrupt or gradual has been debated by many scholars. Taking Hanzhong dialect as the research object, this paper holds that language change is not just gradual but also abrupt, and proves the gradualness from five points and abruptness from four points. Gradualness and abruptness are mainly caused by language contact and immigration. In the long history, Hanzhong has experienced countless natural and man-made disasters such as war and famine. People’s frequent movements inevitably result in language contact and frequent migration further intensify language evolution.

Textual information is the most common type of way by which we can determine what text/texts we are looking for. In order to retrieve text from images the first and foremost step is text detection from the image. Text detection has a wide range of applications such as translation, smart car driving system, information retrieval, indexing of multimedia archives, sign board reading, and countless. Multilingual text detection from images adds an extra complication to a computer vision problem. As India is a multilingual country and therefore multi-script texts can be found almost everywhere. A multi-script text differs in terms of formats, strokes, width, and height. Also, universal features for such an environment are unknown and difficult to determine as well. Therefore, detecting multi-script text from images is an important yet unsolved problem. In this work, we proposed a faster RCNN-based method for detecting English, Hindi, and Gujarati text from Images. Faster RCNN is the state-of-the-art approach for object detection. As it works for objects which are of large size and texts are of smaller size, the parameters are tuned to meet the objective of multi-script text detection. The dataset is created by collecting images as there is no standard dataset available that includes English, Gujarati, and Hindi texts in the public domain.

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

International Journal of Asian Language ProcessingVol. 30, No. 04, 2099001 (2020) Free AccessAuthor Index Volume 30https://doi.org/10.1142/S2717554520990015Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Vol. 30, No. 04 Metrics History PDF download