Search - convert image to text

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[matlab] encipher_image DL : 0: convert image to text-convert image to text..
Date : 2025-12-21 Size : 192kb User : harsha
[matlab] OCR1 DL : 0: this file contain matlab code soure to convert image to text to speach using neuranal network
Date : 2025-12-21 Size : 3.67mb User : marwa
[matlab] text2image DL : 0: here is a code for text to image compression. you can convert your .txt file to jpeg image-here is a code for text to image compression. you can convert your .txt file to jpeg image
Date : 2025-12-21 Size : 154kb User : sowmiya
[matlab] Connected-Component-based-text-region-extraction. DL : 0: The basic steps of the connected-component text extraction algorithm are given below, and diagrammed in Figure 10. The details are discussed in the following sections. 1. Convert the input image to YUV color space. The luminance(Y) value is used for further processing. The output is a gray image. 2. Convert the gray image to an edge image. 3. Compute the horizontal and vertical projection profiles of candidate text regions using a histogram with an appropriate threshold value. 4. Use geometric properties of text such as width to height ratio of characters to eliminate possible non-text regions. 5. Binarize the edge image enhancing only the text regions against a plain black background. 6. Create the Gap Image (as explained in the next section) using the gap-filling process and use this as a reference to further eliminate non-text regions the output. -The basic steps of the connected-component text extraction algorithm are given below, and diagrammed in Figure 10. The details are discussed in the following sections. 1. Convert the input image to YUV color space. The luminance(Y) value is used for further processing. The output is a gray image. 2. Convert the gray image to an edge image. 3. Compute the horizontal and vertical projection profiles of candidate text regions using a histogram with an appropriate threshold value. 4. Use geometric properties of text such as width to height ratio of characters to eliminate possible non-text regions. 5. Binarize the edge image enhancing only the text regions against a plain black background. 6. Create the Gap Image (as explained in the next section) using the gap-filling process and use this as a reference to further eliminate non-text regions the output.
Date : 2025-12-21 Size : 41kb User : Lee Kurian