ocr

问题 I am working on a project to recognize text in Business Cards and map them to appropriate fields.I am using opencv for image processing.I need to feed the preprocessed image to Tesseract-OCR engine for text recognition.This link states that images should have atleast a DPI of 300.My image pixel size is 2560x1536 with 72 DPI. How to increase the DPI to 300? It is also said that it is beneficial to resize image.How to resize my image optimally for good OCR results Tesseract works best on images

问题 I am working on a project to recognize text in Business Cards and map them to appropriate fields.I am using opencv for image processing.I need to feed the preprocessed image to Tesseract-OCR engine for text recognition.This link states that images should have atleast a DPI of 300.My image pixel size is 2560x1536 with 72 DPI. How to increase the DPI to 300? It is also said that it is beneficial to resize image.How to resize my image optimally for good OCR results Tesseract works best on images

问题 I am using Tesseract in iOS 8 for an OCR based app but it incorrectly converts the division "÷" symbol in the image to a plus "+" sign. For example, this image always converts to the text string "8+4+4". It should be "8+4÷4". I've tried using different trained data language files "eng+equ", "ita", adding "÷" to the whitelist, setting the ocr_engine variable to cube, converting image to grayscale or black & white, upsizing the image by 2 and 4 times. Everything I've tried always returns a plus

问题 I am using Tesseract in iOS 8 for an OCR based app but it incorrectly converts the division "÷" symbol in the image to a plus "+" sign. For example, this image always converts to the text string "8+4+4". It should be "8+4÷4". I've tried using different trained data language files "eng+equ", "ita", adding "÷" to the whitelist, setting the ocr_engine variable to cube, converting image to grayscale or black & white, upsizing the image by 2 and 4 times. Everything I've tried always returns a plus

问题 This question has been asked severally and only suggestions are made. Ill comprehend every suggestion hopefully. the dependency is defined in the manifest <meta-data android:name="com.google.android.gms.version" android:value="@integer/google_play_services_version"/> <meta-data android:name="com.google.android.gms.vision.DEPENDENCIES" android:value="ocr" /> and added on the app level compile 'com.google.android.gms:play-services-vision:9.8.0' The test device has sufficient storage which is

问题 I am working on a task to extract the account number from cheque images . My current approach can be divided into 2 steps Localize account number digits (Printed digits) Perform OCR using OCR libraries like Tesseract OCR The second step is straight forward assuming we have properly localized the account number digits I tried to localize account number digits using OpenCV contours methods and using MSER (Maximally stable extremal regions) but didn’t get useful results. It’s difficult to

问题 I am working on an OCR system. A challenge that I'm facing for recognizing the text within ROI is due to the shakiness or motion effect shot or text that is not focus due to angle positions . Please consider the following demo sample If you notice the texts (for ex. the mark as a red), in such cases the OCR system couldn't properly recognize the text. However, this scenario can also come on with no angle shot where the image is too blurry that the OCR system can't recognize or partially

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question I am making an OCR which reads ID cards. After getting region of interest by using YOLO, I am giving that cropped region to Tesseract to read it. As those cropped images are very small and blurry, Tesseract is unable to read them. When it can read them, it

问题 I am using Tesseract OCR to convert scanned PDF's into plain text. Overall it is highly effective but I am having issues with the order that the text is scanned. Documents with tabular data seem to scan down column by column when it seems like the more natural way would be to scan row by row. A very small scale example would be: This is column A, row 1 This is column B, row 1 This is column C, row 1 This is column A, row 2 This is column B, row 2 This is column C, row 2 Is yielding the

问题 I am using Tesseract OCR to convert scanned PDF's into plain text. Overall it is highly effective but I am having issues with the order that the text is scanned. Documents with tabular data seem to scan down column by column when it seems like the more natural way would be to scan row by row. A very small scale example would be: This is column A, row 1 This is column B, row 1 This is column C, row 1 This is column A, row 2 This is column B, row 2 This is column C, row 2 Is yielding the