% img.metaData: Contains all the descriptive information in the image header. % coordinate system transformation, the origin of the IJK coordinate system is (1,1,1) to match Matlab matrix indexing % img.ijkToLpsTransform: pixel (IJK) to physical (LPS, assuming 'space' is 'left-posterior-superior') % Write image and metadata to a NRRD file (see ) There is a Matlab writer for NRRD image files here: PerkLab/SlicerMatlabBridge/blob/master/MatlabCommander/commandserver/nrrdwrite.m function nrrdwrite(outputFilename, img)
The above information summarizes Begumganj gas field as a prospective area in Bengal Basin.Probably the easiest is to save the reconstructed image as a standard NRRD image file. Gas water contact (GWC) is about 3017m below the surface.
The productive horizon covers the interval 2970-3050m. Correlation of this gas sand to the litholog shows that this gas sand belongs to the Bhuban Formation of the Surma Group, a well-known petroleum reservoir in Bangladesh. Wireline logs used to mark this gas zone. In time section, depth converted to 2115ms in Two Way Time (TWT). No fault has been identified in any of the seismic sections. The eastern flank is steeper than the western flank. The contour map shows that the structure of the Begumganj Gas Field is NNW-SSE trending doubly plunging low amplitude slightly asymmetric anticlinal fold. These six (6) seismic sections were interpreted by using PETREL seismic interpretation software. The total length of the studied seismic lines is 93.2 KM. Six (6) 2D seismic lines out of twelve (12) of Begumganj structure were considered for this study. The whole process of text extraction includes main phases-segmentation of characters into line, lines into words and words into characters and then recognition through neural network. The main purpose is to introduce a method for recognition and extraction of handwritten Devnagari characters using segmentation and recognition. We used 2 languages Marathi and English language. We have developed a system to extract text from handwritten documents. Due to very bad styles of writing, a lot of difficulties are faced in recognition process. Those are written in very bad manner, such words cannot be easily read by a machine. Each and every person has its own handwriting style.
Devnagari character has so many possible variations in order, direction and shape of the constituent strokes. Handwritten Devnagari Characters are more difficult for recognition than any other language or English characters. Handwritten character recognition and extraction is an most important part of image processing and pattern recognition.
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