This paper presents a watermarking algorithm for binary images. The original binary image is blurred to a gray-level image and we embed the watermark by biasing the threshold in binarization. A loop is used to control the quality of watermarked images and robustness, and a key is generated for extraction. We employ error correction codes to reduce extraction error. This algorithm can be applied to general binary images except dithered images. Experiments show that the distortion in the watermarked image is not obtrusive and the algorithm provides some degree of robustness.

The accuracy of Optical Character Recognition is deeply affected by the skew of the image.  Skew detection & correction is one of the steps in OCR preprocessing to detect and correct the skew of document image. This research measures the effect of Combined Vertical Projection skew detection method to the accuracy of OCR. Accuracy of OCR is measured in Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent). This research also measures the computational time needed in Combined Vertical Projection with different iteration. The experiment of Combined Vertical Projection is conducted by using iteration 0.5, 1, and 2 with rotation angle within -10 until 10 degrees. The experiment results show that the use of Combined Vertical Projection could lower the Character Error Rate, Word Error Rate, and Word Error Rate (Order Independent) up to 35.53, 34.51, and 32.74 percent, respectively. Using higher iteration value could lower the computational time but also decrease the accuracy of OCR.