Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe

Deep Learning Based Real Time Devanagari Character Recognition

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script’s characters in real time by analyzing the hand movements

A Study of Techniques and Challenges in Text Recognition Systems

The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure

Text, Orality, and Performance in Newar Devotional Music

Dāphā bhajan is a style of devotional song performance practised by Newar men in the towns of the Kathmandu Valley. Although it is now primarily the farming community who maintain it, it originated in the court culture of the Newar kings in the 17th and 18th centuries, and reflects the interests of aristocratic society at that time in devotional literature and music theory. Texts of dāphā songs include compositions attributed to the kings themselves, in old Newari and Maithili, and poetry by Indian authors including Vidyāpati, Nāmdev, Kabīr, Sūrdās and Jayadeva. Transmission to the farming community, among whom literacy and knowledge of the languages concerned were limited, has shifted the balance of attention away from the texts themselves towards the processes of musical performance. As in some other South Asian singing traditions, the generation of intensity through music overwhelms the text, which loses its centrality, its form and even its meaning. The manuscript songbook from which a group sings can no longer be regarded as the vehicle of a written tradition: it is but one element in an oral performance tradition

The Siren of Cirebon: a tenth-century trading vessel lost in the Java Sea

This thesis examines data collected during the salvage of the cargo of a merchant ves-sel foundered in the Java Sea, by a short inscription in a fragment of a bowl and coins dat-ed to around 970 CE. The wreck’s position indicates that the ship was on her way to the island of Java; the verssel herself belongs into the so called ‘lashed-lug and doweled’, Western Austronesian (‘Malayo-Indonesian’) tradition of boat-building. The surviving cargo ranges from Chinese stonewares and Southeast Asian ceramics to Middle Eastern glassware, tin and lead from –proposedly– the Malay Archipelago, and a wide variety of “smaller finds”, most of which can be attributed to the broader area of the western Indian Ocean. The find palpably demonstrates the far-reaching and well-institutionalised trade rela-tions throughout early medieval Asia. It is often assumed that pre-modern Asian com-merce was largely organised in small-scale ventures, the so called “pedlar trade”, and a number of sources indicate structural features of the ships facilitating this commerce that could have supported such a “particularised” exchange. However, a critical assessment of the composition and distribution of the ship’s payload and a virtual reconstruction of the ship and her initial loading pattern reveal that the vessel’s ceramic cargo in all probability was not acquired, handled, and bound to be marketed as a particularised “peddling” ven-ture, but managed by a single authority. The huge amount of ceramics carried on the ves-sel raises questions regarding frequency, volume and modus operandi of maritime ex-changes in tenth-century Southeast Asia, implying that the ship’s tragic voyage was but an attempt at instituting a virtual monopoly in such trade