Introduction
Handwriting recognition, a technology that converts handwritten characters into a digital format, has become increasingly important in various applications. From check processing to signature verification, the ability to automatically recognize and analyze handwritten text offers numerous benefits. In this blog post, we will explore the process of developing a handwriting recognition system using machine learning techniques. Specifically, we will focus on a project that aims to automate the extraction of text from handwritten prescriptions in a medical company.
Problem statement
The objective of this project is to create a neural network model capable of taking images of handwritten prescriptions as input, reading the text from the images, and converting it into digital text. To achieve this, a CNN LSTM model will be implemented. By leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with long short-term memory (LSTM), the model can effectively extract features from the handwritten images and capture temporal dependencies in the sequential data.
Dataset
To train and evaluate the handwriting recognition system, the IAM Handwritten Word Dataset is utilized. This dataset consists of handwritten words in English and serves as a valuable resource for researchers and developers in the field of handwriting recognition. It provides a diverse set of handwritten data that can be used to train and test recognition systems. The dataset includes images of words along with corresponding text files containing information about each word.
Project Components:
Preprocessing Image and Text Data: Image preprocessing techniques are applied to enhance the quality and suitability of the images for analysis. Operations such as filtering, noise removal, contrast enhancement, and geometric transformations are performed to improve image quality. Additionally, text data is processed to ensure compatibility with the model.
Splitting the Data into Training and Testing: The dataset is divided into training and testing sets to evaluate the model's performance. Evaluation metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC) are used to assess the model's ability to make accurate predictions on unseen data.
Implementation of CNN Layers to Extract Features: Convolutional Neural Networks (CNNs) are employed to extract features from the input images. By applying a series of convolutional layers, patterns and features in the images are detected, enabling the network to recognize important characteristics of handwritten text.
Implementation of RNN (Bi-LSTM) Layers to the Sequential Model: Recurrent Neural Networks (RNNs) with Bidirectional Long Short-Term Memory (Bi-LSTM) layers are incorporated into the sequential model. RNNs are designed to work with sequential data, capturing temporal dependencies and allowing for predictions based on context. Bi-LSTM layers enhance the model's ability to understand and interpret the sequential nature of the handwritten text.
CTC Loss and CTC Decode: Connectionist Temporal Classification (CTC) loss and decoding techniques are employed to train the model and decode the output into a sequence of labels. CTC loss treats the problem as a classification task, calculating the probabilities of label sequences based on the output sequence. CTC decoding finds the most likely label sequence, considering repeated or blank labels.
Evaluation of the Model: The model's performance is evaluated using various metrics, including accuracy, precision, recall, F1-score, and AUC. By assessing the model's ability to generalize to unseen data, strengths and areas for improvement can be identified.
The implemented handwriting recognition model achieved an accuracy of 95% and a loss of 0.125% during training. The validation accuracy and loss were recorded at 58% and 35%, respectively. By automating the text extraction process from handwritten prescriptions, the model offers significant time and cost savings for the medical company. Handwriting recognition technology, powered by machine learning, provides enhanced efficiency, accuracy, accessibility, cost savings, versatility, and improved data analysis in various industries and applications.
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