Idea code icr text scanner ren movie#
Idea code icr text scanner ren series#

Once the text is detected, it can be fed to the recognition engine. In some cases, a combination of the two techniques is also employed where the candidate text regions identified by unsupervised methods are validated through a supervised approach. Supervised methods, on the other hand, involve training a learning algorithm with examples of text and non-text regions to discriminate between the two. Unsupervised text detection relies on image analysis techniques to discriminate between text and non-text regions.

Detection of text can be carried out using unsupervised, supervised, or hybrid approaches. The key components of a textual content based indexing and retrieval system include detection of text regions, extraction of text (segmentation from background), identification of script (for multi-script videos), and finally recognition of text (video OCR).

The focus of our present study also lies on the caption text. Caption text is generally correlated with the video content and is mostly applied for semantic retrieval of videos.

Idea code icr text scanner ren movie#

2), on the other hand, is superimposed on the video and typical examples include News tickers, movie credits, and score cards. Scene text is commonly employed for applications like robot navigation and assistance systems for the visually impaired. Examples of scene text include advertisement banners, sign boards, and text on a T-shirt. 1) is captured through camera during the video recording process and may not always be correlated with the content. The textual content in video can be categorized into two broad classes, scene text, and caption text. More specifically, we target a smart retrieval system that exploits the textual content in videos as an index. Among these, the focus of our current study lies on textual content. The term content may refer to the visual content (for example, objects or persons in the video), audio content (the spoken keywords for instance), or the textual content (News tickers, anchor names, score cards, etc.). CBVR systems have been researched and developed for a long time and allow a smarter way of retrieving the desired content. A better and more effective strategy is to search within the actual content rather than simply matching the tags, i.e., content based (image or) video retrieval. The assigned tags, naturally, cannot encompass the rich video content leading to a constrained retrieval. When a content is to be searched, a keyword provided as query is matched with these tags to retrieve the relevant content. Traditionally, videos are mostly stored with user assigned annotations or keywords which are called tags. With such huge collections of data, there is a need to have efficient as well as effective retrieval techniques allowing users retrieve the desired content. A key factor responsible for this enormous increase is the availability of low-cost smart phones equipped with cameras. According to statistics, 300 h of video is being uploaded every minute on the YouTube. In the recent years, there has been a tremendous increase in the amount of digital multimedia data, especially the video content, both in the form of video archives and live streams.

Idea code icr text scanner ren series#

A comprehensive series of experiments is carried out reporting an F-measure of 88.3% for detection while a recognition rate of 87%. A benchmark dataset containing cursive text with more than 13,000 video frame is also developed. Script of the detected textual content is identified using convoluational neural networks (CNNs), while for recognition, we propose a UrduNet, a combination of CNNs and long short- term memory (LSTM) networks. Detection of textual regions in video frames is carried out by fine-tuning deep neural networks based object detectors for the specific case of text detection. More specifically, we target cursive scripts taking Urdu text as a case study. This paper presents a comprehensive framework for detection and recognition of textual content in video frames. The key components of such systems require detection and recognition of textual content which also make the subject of our study. Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams), as well as high level applications like opinion mining and content summarization.