Deep Neural Networks for Text Detection & Recognition in Historical Maps

September 12, 2022

Deep learning and other AI and ML algorithms are widely used by large organizations and startups for various day-to-day activities. One of the uses of these technologies is for text detection and recognition. 

Text detection and recognition have emerged as a significant problem in recent years. This trend has resulted from advancements in the fields of computer vision and machine learning, as well as an increase in applications based on text detection and recognition.

Text detection and recognition from video captions and online pages are also gaining popularity. Much research has been conducted on the topic of text identification and extraction from images. There are various recognition techniques available. Still, a problem with Text detection and recognition is not fully resolved. Text extraction and segmentation from natural scenes are possible. It is still extremely tough to achieve.

In this blog, we will discuss deep convolutional and recurrent neural networks for end-to-end, open-vocabulary text reading on historical maps. A text detection network can anticipate word bounding boxes at any direction and scale. The identified word pictures are then normalized in preparation for use in a robust recognition network.

Figure: Detected text rectangles (predicted baseline in blue)

What are Historical Maps and Problems associated with them?

A historical map is a map that was drawn or printed in the past to promote the study and comprehension of the geography or geographical ideas of the time and place in which it was created or a historical map is a modern map that depicts a past geographical situation or event. 

A map of Delhi published in 1775 is considered historic, and a map made in 2002 to depict Delhi in 1775 is considered historical. Old maps, like old texts, can be difficult to decipher. They differ from modern maps due to their age. They frequently utilize different symbols and were drawn or printed using different procedures than traditional maps. Text on these historical maps can appear in almost any orientation, in a variety of sizes, and alongside graphical components or even add text within the local field of view.

Methodology used

Bringing map document processing into the deep learning era may aid in the elimination of the requirement for complex algorithm creation. Deep-learning models and methodologies can aid in the discovery and interpretation of map text. 

First, because text can appear in practically any orientation on a map, scene text identification algorithms are modified so that the semantic baseline of the text, in addition to its geometric orientation, must be learnt and predicted. 

Second, to make text recognition resistant to text-like graphical distractors, a convolutional and recurrent neural network framework is applied. And finally, a synthetic data creation approach that delivers the many training instances required for accurate recognition without overfitting is detailed.

Structure of the extracted features

This model uses both FOTS (Fast oriented text spotting with a unified network) and ResNet50 (Deep residual learning for image recognition), as a convolutional backbone. Deep Neural Networks process the recovered feature maps, which are 1/32,1/16,1/8, and 1/4 the size of the input image, using the same feature-merging branch as the EAST (efficient and accurate scene text detector) which uses PVANet (lightweight deep neural networks for real-time object detection).

DNN employs bilinear upsampling to repeatedly twice the size of a layer's feature map, then applies a 1X1 bottleneck convolution to the concatenated feature maps, followed by a 3X3 feature-fusing convolution. To construct the final feature map used for the output layers, the merged features are subjected to a final 3X3 convolution.

Output Layers

The output convolutional feature map feeds into three dense, fully-convolutional outputs: a score map for predicting the presence of text, a box geometry map for specifying distances to the boundaries of a rotated rectangle, and the angle of rotation. Each output map is 1/4 the size of the input image.

Loss function

Each output layer requires a different loss function, and the "don't care" regions outside the smaller shrink rectangle but inside the ground truth text rectangle are excluded from all loss function calculations.

Furthermore, places, where two ground truth rectangles overlap, are ignored, eliminating the requirement for any prioritizing strategy in estimating edge distances.


where pi [0, 1] are the score map predictions and ti {0, 1} are the matching ground truth values at each point i.

Rbox: This DNN-based model uses the IoU loss to predict rotated rectangles.

Angle: The cosine loss is simply the rotation angle loss.

The total loss finally is calculated by the below formula:

Historic Map Text Recognition

The DNN-based model uses text recognition to recognise text predicted by the detection branch. Use a layered, bidirectional LSTM trained with CTC loss and built on CNN features. Each convolutional layer employs an activation of the ReLU. Every convolutional network in our network layer uses a paired sequence of 3X3 kernels to increase each Feature's theoretical receptive field.

This DNN-based model uses no padding to trim the initial convolution map to only valid replies. Because the kernel is just 3X3, it erodes only a single pixel from the outside edge while keeping the ability to distinguish fine details at the edges using a relatively small pool of 64 first-layer filters. Following this initial stage, the convolutional maps can essentially learn to generate positive or negative correlations (then ReLU trimmed), implying that zero-padding in the following stages is still appropriate.

After the second convolution of each layer, batch normalization is utilized. Unlike CRNN, including the first two layers adds computation but should reduce the number of iterations required for convergence.

Each character must have at least one horizontal "pixel" in the output feature map. Downsampling horizontally just after the first max pooling step preserves sequence length. As a result, the model is able to distinguish shorter sequences of compact characters. A cropped image of a single character (or digit) can be as small as 8 pixels wide, and two characters can be as small as 10 pixels wide. The input image is normalized to a height of 32 pixels for recognition while keeping the aspect ratio and horizontally extending a clipped section to a minimum width of 8 pixels.

Map word image synthesis

The map word picture synthesis process is divided into three major components. A text layer and a backdrop layer are generated as vector graphics first, then blended and rasterized for post-processing.

Figure: Dynamic map text synthesis pipeline.

Background Layer: Used to aid the recognition model. The synthesizer replicates a wide range of sounds after learning what to ignore. Several cartographic elements make text recognition more difficult The background is separated into several areas, each with its own brilliance. We generate a biased field by picking individuals at random. Anchor points for linear gradients combined with piecewise brightness of the background.

Finally, several types of distractors, thick boundaries, independent lines, and other markers are simulated. Curves (such as rivers, roads, and railroads), grids, parallel lines, and so on curves with changing lengths, ink splotches, points (such as cities) texture and markers (with regular polygons as the textons). A layer of overlapping distractor text is also added for a low likelihood of random locations and orientations.

Text Layer: For rendering, the text caption is selected at random from a list of possible texts or a random sequence of digits, followed by the font (typeface, size, and weight) and letter spacing. Before rotating and presenting the text along a curved baseline, the horizontal scale is changed. To imitate document flaws, circular spots are randomly chopped from the text and the opacity of the layer can be reduced. To simulate inadequate localisation, image padding/cropping is used.

Post-processing: After combining the text with the background layer, we add Gaussian noise to the rasterized image, followed by blur and JPEG compression artifacts.


For automated map comprehension, many tools and domain-specific algorithms have been created. Deep-learning systems can be easily adapted to extract text from complex historical map images given enough training data, as demonstrated by this work. 

We anticipate that georectification-provided pixel-specific lexicons will greatly improve end-to-end outcomes, as they have for cropped word recognition.

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Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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