A handwritten content removing method and device and a storage medium. The handwritten content removing method comprises: acquiring an input image of a text page to be processed, the input image comprising a handwritten region, which comprises a handwritten content (S10); identifying the input image so as to determine the handwritten content in the handwritten region (S11); and removing the handwritten content in the input image so as to obtain an output image (S12).

The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.

An image processing method, an image processing device, an electronic device, and a non-transitory computer readable storage medium are provided. The image processing method includes: obtaining an input image which includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries corresponding to the M character rows according to the intermediate corrected image; and determining the local adjustment reference line and M retention coefficient groups based on the intermediate corrected image and the M character row lower boundaries; determining M local adjustment offset groups corresponding to the M character rows according to the M character row lower boundaries, the local adjustment reference line and the M retention coefficient groups; performing local adjustment on the M character rows in the intermediate corrected image according to the M local adjustment offset groups to obtain the target corrected image.

Systems and methods for mobile enrollment in automated clearing house (ACH) transactions using mobile-captured images of financial documents are provided. Applications running on a mobile device provide for the capture and processing of images of documents needed for enrollment in an ACH transaction, such as a blank check, remittance statement and driver's license. Data from the mobile-captured images that is needed for enrolling in ACH transactions is extracted from the processed images, such as a user's name, address, bank account number and bank routing number. The user can edit the extracted data, select the type of document that is being captured, authorize the creation of an ACH transaction and select an originator of the ACH transaction. The extracted data and originator information is transmitted to a remote server along with the user's authorization so the ACH transaction can be setup between the originator's and receiver's bank accounts.

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.

An image object labeling method and system are disclosed. The method is executed by a processor coupled to a memory and includes providing an image file; detecting at least one first object on the image file and generating at least one graphic block and attribute thereof; performing a binarization process to present a first graphic feature on a region containing the graphic block in the image file and a second graphic feature on the rest region; combining the results of detecting and the binarization process, and filtering the image file through several gradually reduced masks to show several separated graphic components until number of the separated graphic components and the at least one graphic block are the same; and assigning a label to the separated graphic components according to the attribute, alternatively, outputting a message, receiving a command for adjusting the graphic block and attribute thereof, as the labels.

The present disclosure discloses a method for recognizing text, an apparatus and a terminal device. The method for recognizing text includes: acquiring a sample text dataset, preprocessing text image in the sample text dataset, and generating a label image; inputting the label image into a text recognition model for training, extracting image features, performing down-sampling, restoring an image resolution, normalizing an output probability for the last layer using a sigmoid layer to output a multiple prediction maps with different scales, and optimizing a loss function of the text recognition model to obtain a trained text recognition model; preprocessing a text image to be recognized, inputting the text image to be recognized which is preprocessed into the trained text recognition model, and outputting a clear-scale prediction map; and analyzing the clear-scale prediction map to obtain a text sequence of the text image to be recognized.

The present disclosure discloses a method for recognizing text, an apparatus and a terminal device. The method for recognizing text includes: acquiring a sample text dataset, preprocessing text image in the sample text dataset, and generating a label image; inputting the label image into a text recognition model for training, extracting image features, performing down-sampling, restoring an image resolution, normalizing an output probability for the last layer using a sigmoid layer to output a multiple prediction maps with different scales, and optimizing a loss function of the text recognition model to obtain a trained text recognition model; preprocessing a text image to be recognized, inputting the text image to be recognized which is preprocessed into the trained text recognition model, and outputting a clear-scale prediction map; and analyzing the clear-scale prediction map to obtain a text sequence of the text image to be recognized.

According to an exemplary embodiment, a method for pre-cropping digital image data includes: dividing the digital image into segments; computing a color value distance between corresponding pixels of neighboring segments of the digital image; comparing the color value distance(s) against a minimum color distance threshold; clustering neighboring segments having a color value distance less than or equal to the minimum color distance threshold; computing a connected structure based on the clustered segments; computing a polygon bounding the connected structure; comparing a fraction of segments included in the connected structure and the polygon, relative to a total number of segments in the digital image, to a minimum included segment threshold; and in response to determining the fraction of segments in the connected structure and the polygon, relative to the total number of segments meets or exceeds a minimum included segment threshold, cropping the digital image based on edges of the polygon.

Computer-implemented methods for detecting objects within digital image data based on color transitions include: receiving or capturing a digital image depicting an object; sampling color information from a first plurality of pixels of the digital image, wherein each of the first plurality of pixels is located in a background region of the digital image; assigning each pixel a label of either foreground or background using an adaptive label learning process; binarizing the digital image based on the labels assigned to each pixel; detecting contour(s) within the binarized digital image; and defining edge(s) of the object based on the detected contour(s). Corresponding systems and computer program products configured to perform the inventive methods are also described.