Detection of Text in Videos Using Discrete Wavelet Transform and Gradient Difference

Text detection in video frames provide highly condensed information about the content of the video and it is useful for video seeking, browsing, retrieval and understanding video text in large video databases. In this paper, we propose a hybrid method that it automatically detects segments and recognizes the text present in the video. Detection is done by using laplacian method based on wavelet and color features. Segmentation of detected text is divided into two modules Line segmentation and Character segmentation. Line segmentation is done by using mathematical statistical method based on projection profile analysis. In line segmentation, multiple lines of text in video frame obtained from text detection are segmented into single line. Character segmentation is done by using Connected Component. Analysis (CCA) and Vertical Projection Profile Analysis. The input for character segmentation is the line of text obtained from line segmentation, in which all the characters in the line are segmented separately for recognition. Optical character recognition is Processed by using template matching and correlation technique. Template matching is performed by comparing an input character with a set of templates, each comparison results in a similarity measure between the input characters with a set of templates. After all templates have been compared with the observed character image, the character’s identity is assigned with the most similar template based on correlation. Eventually, the text in video frame is detected, segmented, and processed to OCR for recognition.

Prosiding Snatika_Tinjauan Metode Pengenalan Penulisan Tangan_Henny Hamsinar

Perkembangan penelitian tentang teknologi OCR (optical Character Recognition) masih menjadi tren penelitian saat ini. Pada Penelitian ini akan membahas perkembangan pengenalan tulisan tangan dan tantangan dari penelitian-penelitian yang telah dilakukan sebelumnya. Ada beberapa proses penting yang dilalui dalam teknologi OCR antara lain preprocessing, segmentasi, ekstraksi fitur, klasifikasi. Setiap proses yang dilalui akan menentukan hasil akurasi yang akan dicapai. Proses segmentasi sangat menentukan hasil ekstraksi fiturnya. Dominant point detection dan projection profile histogram merupakan metode-metode yang telah digunakan pada proses segmentasi. NN, SVM, FFANN merupakan metode yang banyak dipakai dalam proses klasifikasi.

Character Segmentation Of Degraded Odia Script

Optical Character Recognition (OCR) is a field which converts printed text into computer understandable format that is editable in nature. Odia is a regional language used in Odisha, West Bengal & Jharkhand. It is used by over forty million people and still counting. With such large dependency on a language makes it important, to preserve its script, get a digital editable version of odia script. We propose a framework that takes computer printed odia script image as an input & gives a computer readable & user editable format of same, which eventually recognizes the characters printed in input image. The system uses various techniques to improve the image & perform Line segmentation followed by word segmentation & finally character segmentation using horizontal & vertical projection profile.

CHARACTER IMAGE SEGMENTATION OF JAVANESE SCRIPT USING CONNECTED COMPONENT METHOD

Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%.

Analysis of Segmentation Methods for Brahmi Script

Segmentation is an important step for developing any optical character recognition (OCR) system, which has to be redesigned for each script having, non-uniform nature/property. It is used to decompose the image into its sub-units, which act as a basis for character recognition. Brahmi is a non-cursive ancient script, in which characters are not attached to each other and have some spacing between them. This study analyses various segmentation methods for different scripts to develop the best suitable segmentation method for Brahmi. MATLAB software was used for segmentation purpose in the experiment. The sample data belongs to Brahmi script-based ‘Rumandei inscription’. In this paper, we discuss a segmentation methodology for distinct components, namely text lines, words and characters of Rumandei inscription, written in Brahmi script. For segmenting distinct components of inscription different approach were used like horizontal projection profile, vertical projection profile and Relative minima approach. This is fundamental research on an inscription based on Brahmi script, which acts as a foundation for developing a segmentation module of an OCR solution/system of similar scripts in future. Information search and retrieval is an important activity of a library. So, to ensure this support for digitised documents written in ancient script, their character recognition is mandatory through the OCR system.