Video OCR Research Articles

Implementing Video OCR along with SWT Technique for Video indexing and Analysis

Implementing Video OCR along with SWT Technique for Video indexing and Analysis

Open Datasets and Tools for Arabic Text Detection and Recognition in News Video Frames

Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

NF-SAVO: Neuro-Fuzzy system for Arabic Video OCR

In this paper we propose a robust approach for text extraction and recognition from video clips which is called Neuro-Fuzzy system for Arabic Video OCR. In Arabic video text recognition, a number of noise components provide the text relatively more complicated to separate from the background. Further, the characters can be moving or presented in a diversity of colors, sizes and fonts that are not uniform. Added to this, is the fact that the background is usually moving making text extraction a more intricate process. Video include two kinds of text, scene text and artificial text. Scene text is usually text that becomes part of the scene itself as it is recorded at the time of filming the scene. But artificial text is produced separately and away from the scene and is laid over it at a later stage or during the post processing time. The emergence of artificial text is consequently vigilantly directed. This type of text carries with it important information that helps in video referencing, indexing and retrieval.

Video OCR for Video Indexing

Video OCR is a technique that can greatly help to locate the topics of interest in video via the automatic extraction and reading of captions and annotations. Text in video can provide key indexing information. Recognizing such text for search application is critical. Major difficult problem for character recognition for videos is degraded and deformated characters, low resolution characters or very complex background. To tackle the problem preprocessing on text image plays vital role. Most of the OCR engines are working on the binary image so to find a better binarization procedure for image to get a desired result is important.Accurate binarization process minimizes the error rate of video OCR.

Complementary combination of holistic and component analysis for recognition of low-resolution video character images

Video OCR aims at extracting text from video images in order to understand the context of the video. Video character images are usually given in low resolution with unique characteristics such as large stroke distortion, font variation, and variable size. Therefore, recognizing such characters in video images is very challenging. This is particularly true in the case of Chinese and Korean languages, where characters have complicated shapes and the number of classes (characters) is very large. In this paper, we propose a complementary combination of two recognizer approaches: a holistic approach and a component analysis. The holistic approach utilizes the global shape information of a character image to recognize a radical at a specific location of the character. On the contrary, the component analysis utilizes a detailed local shape of a segmented radical image to recognize the radical. The former is effective for character degradation whereas the latter is strong at processing ambiguous characters and font variations. In an evaluation of 50,000 video character images of Korean script, the proposed method achieved 96.5% accuracy. From this, we may draw a conclusion that the proposed method works well even with low quality images of complicated characters.

Complementary video and audio analysis for broadcast news archives

Abstract The Informedia Digital Video Library system extracts information from digitized video sources and allows full content search and retrieval over all extracted data. This extracted 'metadata' enables users to rapidly find interesting news stories and to quickly identify whether a retrieved TV news story is indeed relevant to their query. This article highlights two unique features: named faces and location analysis . Named faces automatically associate a name with a face, while location analysis allows the user to visually follow the action in the news story on a map and also allows queries for news stories by graphically selecting a region on the map. 1 The Informedia Digital Video Library Project The Informedia Digital Video Library project [1], initiated in 1994, uniquely utilizes integrated speech, image and natural language understanding to process broadcast video. The project’s goal is to allow search and retrieval in the video medium, similar to what is available today for text only. To enable this access to video, fast, high-accuracy automatic transcriptions of broadcast news stories are generated through Carnegie Mellon’s Sphinx speech recognition system and closed captions are incorporated where available. Image processing determines scene boundaries, recognizes faces and allows for image similarity comparisons. Text visible on the screen is recognized through video OCR and can be searched. Everything is indexed into a searchable digital video library [2], where users can ask queries and retrieve relevant news stories as results. The