1 / 6

Processing invoices using Pytorch

At the bottom of the CRNN, the convolutional layer automatically extracts the sequence of features from each input image. Above the convolutional network, a circular network is constructed to predict each frame of the feature sequence output by the convolutional layer. Each frame prediction of the loop layer is converted to a tag sequence using a transcription layer on top of the CRNN. Although CRNN consists of different types of network architectures (such as CNN and RNN), joint training can be performed through a loss function.

byteridge
Download Presentation

Processing invoices using Pytorch

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Byteridge Processing invoices using Pytorch

  2. Byteridge Introduction Problem statement • The dataset has 1000 whole scanned receipt images. Each receipt image contains around about four key text fields, such as goods name, unit price and total cost, etc. • The text annotated in the dataset mainly consists of digits and English characters. An example scanned receipt is shown below:

  3. Byteridge Tasks • Scanned Receipt Text Localisation: The aim of this task is to accurately localize texts with 4 vertices. • Scanned Receipt OCR: The aim of this task is to accurately recognize the text in a receipt image. No localisation information is provided, or is required. • Key Information Extraction from Scanned Receipts: The aim of this task is to extract texts of a number of key fields from given receipts, and save the texts for each receipt image in a json file. We will go a little ahead and deploy this as a Flask app and make a generic tool out of the models.

  4. Byteridge Tensors Tensors are similar to numpy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing. Tensors are multi dimensional Matrices. torch.Tensor(x,y) This will create a X by Y dimensional Tensor that has been instantiated with random values. To Create a 6×4 Tensor with values randomly selected from a Uniform Distribution between -1 and 1, torch.Tensor(6, 4).uniform_(-1, 1) Tensors have a size attribute that can be called to check their size print(x.size())

  5. Byteridge CTPN • CTPN stands for Connectionist Text Proposal NetworkCTPN is a deep learning method that accurately predicts text lines in a natural image. It is an end-to-end trainable model which consists of both CNN and RNN layers. • This algorithm detects text or words in any kind of image including both scanned documents and natural images. It accurately localizes text lines in natural image and detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps. • CTPN works reliably on multi-scale and multi-language text without further post-processing, departing from previous bottom-up methods requiring multi-step post-processing.It is computationally efficient with 0:14s/image, by using the very deep VGG16 model.

  6. Byteridge CONTACT US Contact:+91 40491 74522 Mail : info@byteridge.com Address: WeWork Rajapushpa Summit, Financial District, Hyderabad, Telangana Visit here for more : https://www.byteridge.com/expert-opinions/processing-invoices-using-pytorch/

More Related