The present embodiments relate to a language identification system for predicting a language and text content of text lines in an image-based document. The language identification system uses a trainable neural network model that integrates multiple neural network models in a single unified end-to-end trainable architecture. A CNN and an RNN of the model can process text lines and derive visual and contextual features of the text lines. The derived features can be used to predict a language and text content for the text line. The CNN and the RNN can be jointly trained by determining losses based on the predicted language and content and corresponding language labels and text labels for each text line.

The present embodiments relate to a language identification system for predicting a language and text content of text lines in an image-based document. The language identification system uses a trainable neural network model that integrates multiple neural network models in a single unified end-to-end trainable architecture. A CNN and an RNN of the model can process text lines and derive visual and contextual features of the text lines. The derived features can be used to predict a language and text content for the text line. The CNN and the RNN can be jointly trained by determining losses based on the predicted language and content and corresponding language labels and text labels for each text line.

Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

Arrangements detailed herein are directed to a requirement test system for a cellular network. Arrangements allow for technical requirements from digitized documents to be extracted. Each of the technical requirements are mapped in a technical requirement repository to at least one cellular network test to be performed on the cellular network. The execution of the cellular network test is performed to determine compliance with the technical requirements extracted from the digitized document.

Arrangements detailed herein are directed to a requirement test system for a cellular network. Arrangements allow for technical requirements from digitized documents to be extracted. Each of the technical requirements are mapped in a technical requirement repository to at least one cellular network test to be performed on the cellular network. The execution of the cellular network test is performed to determine compliance with the technical requirements extracted from the digitized document.

A bulk of electronic documents are uploaded to a document management system. A document managing module within the document management system detects if a uploaded document contains distinct sections, each of which contains substantially one single language. If the distinct sections can be separated in a clean manner, the module divides the uploaded document into multiple files based on the multiple languages in the distinct sections, each of the multiple files contains a single language. The multiple files are then processed with OCR operations to generate multiple sectioned PDF documents. All the multiple sectioned PDF sections are then combined together to restore the original uploaded document in a searchable PDF form.

A bulk of electronic documents are uploaded to a document management system. A document managing module within the document management system detects if a uploaded document contains distinct sections, each of which contains substantially one single language. If the distinct sections can be separated in a clean manner, the module divides the uploaded document into multiple files based on the multiple languages in the distinct sections, each of the multiple files contains a single language. The multiple files are then processed with OCR operations to generate multiple sectioned PDF documents. All the multiple sectioned PDF sections are then combined together to restore the original uploaded document in a searchable PDF form.

Provided is a method for suggesting a caption for an image, comprising: receiving the image; determining a plurality of impacting categories associated with a plurality of contextual keywords for the image, each of the plurality of impacting categories representing a sentiment associated with the plurality of contextual keywords; grouping the plurality of contextual keywords into a plurality of groups based on the plurality of impacting categories; determining an order associated with the plurality of contextual keywords, based on a pre-determined impacting function; generating at least one caption by processing each contextual keyword, based on the order associated with the plurality of contextual keywords; determining a priority value associated with each of the at least one caption based on information associated with the corresponding caption, a user profile, and the image; and suggesting the caption from the at least one caption based on the priority value.

Provided is a method for suggesting a caption for an image, comprising: receiving the image; determining a plurality of impacting categories associated with a plurality of contextual keywords for the image, each of the plurality of impacting categories representing a sentiment associated with the plurality of contextual keywords; grouping the plurality of contextual keywords into a plurality of groups based on the plurality of impacting categories; determining an order associated with the plurality of contextual keywords, based on a pre-determined impacting function; generating at least one caption by processing each contextual keyword, based on the order associated with the plurality of contextual keywords; determining a priority value associated with each of the at least one caption based on information associated with the corresponding caption, a user profile, and the image; and suggesting the caption from the at least one caption based on the priority value.

Methods for automatic language detection for handwritten text are performed by systems and devices. Such automatic language detection is performed prior to sending representations of the handwritten text to a language recognition engine. Handwritten inputs including one or more writing strokes are received from an input interface, and are associated with coordinates of the inputs and times that the inputs are made. The handwritten inputs are grouped into words based on the coordinates and times. Writing strokes are normalized, and then the words are individually transformed to generate language vectors, such as through a recurrent neural network. The language vectors are used to determine language probabilities for the handwritten inputs. Based on the language probabilities, the handwritten inputs are provided to a specific language recognition engine to determine the language thereof prior to translation or transcription.