Text In Video Frames Research Articles

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

يعد تشفير النص في الفيديو أداة حيوية في العديد من الصناعات حيث تكون حماية المعلومات الحساسة هي الأكثر أهمية. عادةً ما تواجه الخوارزميات وطرق الكشف الآلي صعوبة في تحديد موقع النص المخفي في إطارات الفيديو. تزيد هذه المقاومة من أمان المعلومات المخفية لأنه من الصعب على الأطراف غير المدعوة اكتشاف وجود نص مشفر. كما أنه يجمع بين المحتوى المكتوب والمرئي لتقديم كلا النوعين من المعلومات في وقت واحد وبسلاسة. في هذا البحث ، نقدم طريقة جديدة لتشفير النص داخل الفيديو باستخدام خوارزمية التشفير Salsa20 و Harris Corner Detection. الهدف من هذه الدراسة هو زيادة سرية وأمن المعلومات النصية المضمنة في محتوى الفيديو ، وهناك خطوتان أساسيتان في المنهجية المقترحة. لتحديد الزوايا أو النقاط المهمة داخل إطارات الفيديو ، تم استخدام طريقة Harris Corner Detection لأول مرة. تعمل هذه الزوايا كنقاط ميزة قوية يمكن استخدامها لدمج النص. تساعد طريقة Harris Corner Detection في تحديد الأماكن التي قد يتم إخفاء النص بها بدقة ، وذلك دون التقليل من جودة الصورة المرئية الإجمالية للفيديو ، وثانيًا ، يتم إنشاء مفتاح التشفير باستخدام تقنية التشفير Salsa20. Salsa20 هو تشفير دفق شائع يوفر تشفيرًا موثوقًا وتشغيلًا فعالاً. يتم تحويل النص المضمن إلى نص تشفير بواسطة XOR باستخدام مفتاح التشفير قبل تضمينه في الفيديو ، مما يضمن سريته وحمايته من الوصول غير المصرح به. تكشف مقارنة الطريقة المقترحة مع طرق تشفير النص الحالية عن عدد من المزايا. تدمج تقنية Harris Corner Detection النص في إطارات الفيديو بحيث يتم دمجها بشكل أقل وضوحًا ، مما يجعل من الصعب على الخصوم تحديد وجود المعلومات المخفية. يضمن استخدام Salsa20 أيضًا المستوى العالي من الأمان والحماية للنص المشفر ، والدفاع عنه ضد الهجمات المحتملة. يتم تقييم النظام المقترح باستخدام MSE و PSNR والارتباط و NPCR و UACI والإنتروبيا ، وتوفر مقاييس التقييم هذه نتائج ممتازة.

The use of Scene Text Detection methods for news crawl extraction

News crawl extraction is a subset of media monitoring. It is not directly related to the broadcast itself and not spoken by any anchor or guest - therefore its content cannot be inspected via speech-to-text tools. News crawl content is only available as text in video frames so its extraction can be done using Scene Text Detection techniques. This work tests the most used deep learning Scene Text Detection methods (both regression and semantic segmentation approaches) to see if they can be used as a base in news crawl extraction. Our investigation shows that regression based model CTPN performs better for our particular purpose that the semantic segmentation based EAST and CRAFT models.

Arabic text detection in news video using RetinaNet

Embedded text in video frames represents an interesting source of high level information especially for semantic indexing and retrieval applications. In this paper, we propose a novel approach for Arabic text detection in news videos using a deep learning method called RetinaNet. It is based in two steps. The first one aims to extract features using residual network (Res-Net) and a pyramidal feature network (FPN). In the second step, we use two fully convolutional networks (FCN), one is for the classification task and the other for the bounding box regression task. Experiments show that the proposed method can detect text regions effectively and provides a more satisfactory results compared to other existing methods.

Deep Features based Multilingual Text Detection and Recognition in Video Frames

One of the major causes for achieving poor text detection results in video frames are complex background, illumination and de-blurring of the frames and it is also important challenges for the researchers to overcome such problems. Therefore, motivated from this kind of observation from recent survey, we propose a text detection method based on Deep Neural Networks known as TextBoxes which is capable of detecting text in video frames with improved performance when compared to state-of-the-art techniques. In parallel this we also propose a Text candidate detection for video frames and scene images by extracting words based on Automatic Window Detection by making use of Discrete Wavelet Transform (DWT) with the sliding window for extracting high frequency sub bands for each sliding window. K-means clustering technique has been used to obtain the text components and to decrease the background complexity and noise. Six-layer convolutional neural network model has been designed to recognize the text in multilingual images. Experiments for text detection are done on our own multilingual South Indian database, ICDAR-2015 Videos, YVT videos, SVT, and MSRA Scene datasets and demonstrated in terms of Recall, Precision and F-measure and for recognition ICDAR-2015 Videos, ICDAR 2011 and SVT scene images.

Niblack’s binarization method and its modifications to real-time applications: a review

Local binarization methods deal with the separation of foreground objects (textual content) and background noise (non-text) specifically at the pixel level. This is a much-explored field in the domain of documents image-processing that tends to separate the textual content from a degraded document. Since three decades, many local binarization methods have been developed to binarize documents images suffering from severe deteriorations. This paper presents a review of local binarization methods that are developed based on Niblack’s binarization method (NBM, developed in 1986) only. Further, this paper is a review of local binarization methods having more or less modifications to the original Niblack’s method, depending on the requirements of their model and the processed output. The modifications to NBM can be seen in various applications, such as deteriorated documents image binarization, manuscripts restoration, finding texts in video frames, revealing engraved wooden stamps, vehicle license plate number recognition, stained cytology nuclei detection and product barcodes reading. However, there could be a possibility of other applications using NBM with modifications based on the input images and the applications’ requirements.

New Fourier-Statistical Features in RGB Space for Video Text Detection

In this paper, we propose new Fourier-statistical features (FSF) in RGB space for detecting text in video frames of unconstrained background, different fonts, different scripts, and different font sizes. This paper consists of two parts namely automatic classification of text frames from a large database of text and non-text frames and FSF in RGB for text detection in the classified text frames. For text frame classification, we present novel features based on three visual cues, namely, sharpness in filter-edge maps, straightness of the edges, and proximity of the edges to identify a true text frame. For text detection in video frames, we present new Fourier transform based features in RGB space with statistical features and the computed FSF features from RGB bands are subject to K-means clustering to classify text pixels from the background of the frame. Text blocks of the classified text pixels are determined by analyzing the projection profiles. Finally, we introduce a few heuristics to eliminate false positives from the frame. The robustness of the proposed approach is tested by conducting experiments on a variety of frames of low contrast, complex background, different fonts, and sizes of text in the frame. Both our own test dataset and a publicly available dataset are used for the experiments. The experimental results show that the proposed approach is superior to existing approaches in terms of detection rate, false positive rate, and misdetection rate.