The software scans a document, weeding out artifacts and noise, like dust or stray marks. It zooms in on letters, numbers and other characters that need translating. Then it analyzes whole words. That means the practice dataset is highly specialized, leading to much more accurate interpretations of industry-specific words and their context.
Because it saves its compute power for this narrower sphere, our OCR solution is much better equipped to accurately interpret problem cases and unusually messy handwriting. Or the AI might learn over time that certain letters are frequently transposed or condensed in similar ways when handwritten.
In any document, we also focus our attention on identifying the key area for analysis first. A convolutional discriminator based on BigGAN architecture is used to classify if the generate style of images is looking fake or real.
The discriminator doesn't rely on character level annotations and hence is not based on a class conditional GAN. The advantage of this being there is no need for labelled data and hence data from unseen corpus which is not part of training data can be used for training discriminator. Along with the discriminator a text recognizer R is trained to classify if the text generated makes real world sense or if it's gibberish. The recognizer is based on CRNN architectures with the recurrent head removed to make the recognizer a little weaker and not recognize text even if it is unclear.
The text generated in the output of R is compared with the input text given to generator and a corresponding penalty is added to loss function. Character Error Rate :- It is computed as the Levenshtein distance which is the sum of the character substitutions Sc , insertions Ic and deletions Dc that are needed to transform one string into the other, divided by the total number of characters in the groundtruth Nc.
Word Error Rate :- It is computed as the sum of the word substitutions Sw , insertions Iw and deletions Dw that are required to transform one string into the other, divided by the total number of words in the groundtruth Nw. Now let's see how we can train our own handwritten text recognition model. We will be training on IAM dataset but you can train the model on your own dataset as well. Let's discuss the steps involved in setting this up. To download IAM dataset register from here.
Once registered download words. This contains a dataset of handwritten word images. Also download the annotation file words. The above shows how AIM dataset folder structure looks. Here a01, a02 etc. Each sub-folder has a set of images having name of the folder added as a prefix to it's file name. In addition we need an annotation file to mention the paths to the images files and the corresponding transcriptions.
Consider for example the above image with text nominating, the below would be the representation in annotation file words.
We shall be using the CRNN code from here to train our model. Follow the steps from below to prepare the data. Some of the predictions can be seen in the below figure. The model is able to predict the characters accurately to a great extent but it suffers in few cases such as awfully is predicted as anfully, stories is predicted as staries.
These issues can be resolved by employing a language model as a post processing step along with the decoder which can generate meaningful words and rectify simple mistakes. Although there have been significant developments in technology which help in better recognition of handwritten text, HTR is a far from a solved problem compared to OCR and hence is not yet extensively employed in industry.
Nevertheless with the pace of technology evolution and with the introduction of models like transformers, we can expect HTR models to become a commonplace soon. To catch more research on this topic you can get started from here. Schedule a Call. Get Started. Please enter at least 3 characters 0 Results for your search.
May we suggest a tag? There are two types of handwriting recognition. First is the older of the two, known as offline handwriting recognition. This is where the handwritten input is scanned or photographed and given to the computer. For instance, stroke direction and pen weight. There are a few different ways that handwriting recognition works. One way for this to happen is handwriting OCR, or optical character recognition. This is where the computer zooms in on each character and identifies it by comparing it to a database of known characters and words.
It reduces the range of differences in the writing and keeps each character distinct and separate from the last. Handwriting recognition tends to have problems when it comes to accuracy. How, then, is a computer going to do it? We present a Neural Network based Handwritten Text Recognition HTR model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation.
Handwriting Recognition Semantic Segmentation. This paper introduces a novel method to fine-tune handwriting recognition systems based on Recurrent Neural Networks RNN. Fine-tuning Handwriting Recognition. In this work, we demonstrate how to train an HTR system with few labeled data.
Handwriting Recognition. Recurrent neural networks RNNs are a powerful model for sequential data. Handwriting Recognition Speech Recognition.
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