I'm working on a large corpus of french daily newspapers from the 19th century that have been digitized and where the data are in the form of raw OCR text files (one text file per day). In terms of size, one year of issues is around 350 000 words long. What I'm trying to achieve is to detect the different articles that form a newspaper issue. Knowing that an article can be two or thee lines long or very much …

I just read paper about cnn + rnn for text recognition. The labels of dataset is tensor of char index (e.g [0, 1, 2 ] for image with label "abc"). Since the label of each input has different length do I need to convert the label to sparse tensor value ? since the paper does not mention about it.

I'm thinking about an OCR system for digitalizing receipts. On the input system would take a picture of receipt and then return classified data (total_sum = Y, date = X, etc.). My question is regarding how to start. My initial thought was that I should start with detecting classes (name of the shop, receipt id etc.) on image and splitting it, then I would send parts of image OCR. My second idea is more NLP based. I would normally pass …

I would like to be able to utilize Orange to OCR pictures with pytesseract. I have been able to create simple code in the Python Script widget in order to read one image at a time, but I want to be able to bring the images in with import images widget, and use the python script just to read them and provide an output. When it comes to the output, I would like to be able to save the text …

For example, if the camera pans across a line of text such that no one frame contains the entire line, but the line is fully captured in the video. I'm aware OCR is quite mature, but I haven't found any approaches to this problem.

Lately, I have been largely inspired by this https://rossum.ai/, which is able to extract text from invoice documents. Do you have any ideas on how this could be implemented? It's clear that they did a lot of research to reach this performance level, but in my case I am interested in the overall approach to such problems. If I understand correctly, the first part of the pipeline is to extract different blocks from the document. In that case, is object …

I have a model for OCR, which after 2-3 epochs gives the same output. When I predicted the values and looked at the output for each layer I realized that all layers after the 1st layer in the LSTM block output the same values no matter the output. Here is the model (or the parts related to the problem): Processing = layers.Reshape((12,9472))(encoder) Processing = layers.Dense(128, activation='relu')(Processing) lstm = layers.Bidirectional(layers.LSTM(256, return_sequences = True))(Processing) lstm = layers.Bidirectional(layers.LSTM(128, return_sequences = True))(lstm) lstm = …

I have used the MNIST data set many times to train models for digit recognition based on object character recognition (OCR). I am now trying to do the same but with a data set of svg paths.. I am trying to find an MNIST equivalent of a digital path / svg based data set. Here is a sample: the svg <path d="m233.5,119.4375c-1,-1 -3.025818,-1.320366 -5,-1c-3.121445,0.506538 -8.191559,0.090805 -15,2c-14.665848,4.112541 -23.266006,8.139008 -31,11c-6.291519, 2.327393 -11.679474,6.571106 -14,11c-1.467636,2.801086 -2,7 -2,10c0,4 -0.610916,8.03746 0,13c0.503769,4.092209 2.877655,8.06601 4,10c1.809723,3.118484 4.718994,6.310211 8,9c5.576645,4.571762 11.887314,5.376694 …

I have about 200,000 PDFs made up of 20 different designs. i.e In an organization, different (20) departments issue monthly award submission requirements. Each department has its own document format. These documents are collected by the organization. Now I need to extract the paragraphs, bullet points, or sentences from each of these PDFs, organize it properly, specify if it is a requirement or not (label the data), and store it in storage. This process needs to be repeatable/automated for any …

I need to do entity recognition on a set of text data. There are two important aspects here text data is produced from an OCR which infact has tons of mis-spelled words. For example it produces Stabhylooocjs lve vit Salnomela can not lve on cober surfcs chikens gut i ful of Strebt0cus but not if hey get fd wih Aectat Nucopactirun is he seond bet berklorabe producer instead of Staphylococcus live with Salmonella can not live on copper surfaces Chickens …

I am currently working on a project where I need to detect bold text on a multi font-size image (so no mathematic morphology possible). This detection will be used in parallel of an OCR system (with tesseract) to detect which information (in bold) are important in a document. I already tested the wordFontAttribute() function of tesseract but it is inconsistent : it provide me poor results of bold detection and decresease the performance of my OCR system because to use …

Given an image of a floor plan, is there a known algorithm I can use to understand measurements of all apartments present ? (for example, that means in the attached picture understanding there are 4 apartments, and reading the measurements specified for each one? I guess reading numbers (symbols) from an image should not be an issue, but the challenge is recognising the walls and understanding what apartment we're looking at out of the 4.

The task is to detect rotated alphanumeric characters embedded on colored shapes. We will have an aerial view of the object (from a UAS: Unarmed Aerial System). Something of this sort: (One Uppercase alphabet/number per image). We have to report 5 features: Shape ,Shape colour, alphanumeric , alphanumeric colour and alphanumeric orientation. Right now, I am focusing on just detecting the alphanumeric and the shape. Approach 1 : I used a pre trained EAST model for text detection, along with …

I'm trying to extract resume (PDF) data. resumes always tend to follow a structure. so if you see some numbers in a cv; according to the context, we could tell whether its a telephone number, a birthday, or a date period. if I can classify/identify one entity then that would increase my ability to classify an entity near to it. I'm still a newbie and appreciate if anyone could give me any thoughts on approaching this problem. what kind of …

I am building a model for reading receipts from their mobile snapshots. After the receipt is OCR'd, I plan to use a variation on LayoutLM for entity extraction. Entities are: "quantity", "price-per-unit", "product-name", "items-price", etc. What is the best model to consider to link all these entities into a single receipt item, so the final result looks like: "items": [ {"product": ..., "unit_price": ..., "price_paid": ..., "quantity": ..., }, ... ]

I have a problem where I need to predict some integers from an image. The problem is that this includes some negative integers too. I have done some reasearch and came accross Poisson which does count regression, however this does not work due to me also needing to predict some negative integers too, resulting in Poisson output nan as its loss. I was thinking of using Lambda to round the output of my model however this resulted in this error: …

I want to read this image text

I am thinking of training a model to automatically extract information from more or less structured documents like invoices. Here are the main challenges regarding this task: In fact, even though invoices are often called "structured" documents, there is a lot of variance in their layouts depending on the field, company, and other factors, which makes it almost impossible to achieve great results with pattern matching. When relying on text, the most straightforward solution to get textual information from documents …

I have to recognize text from images and trying to understand CNN+BiLSTM+CTC architecture. I have text images in .jpg format but How should I generate its transcription like in .txt or in .xml format? Where I will feed ground truth in this architecture like along with text images in CNN or in RNN or in CTC layer? I haven't found any clear explanation about OCR ground truth file format. Any help will be highly appreciated.

I would like to train an optical mark recognition model (OMR) to detect and classify the ticked/unticked state of checkboxes in documents. Does anyone know where I can have access to a publicly available scan document database containing text and ticked/unticked checkboxes? Thanks.