File size: 4,324 Bytes
d0b8e8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | import gleam/http/request
import gleam/http.{Post}
import gleam/float
pub type OcrProvider {
GoogleVision
AzureComputerVision
TesseractOcr
}
pub type HandwritingData {
HandwritingData(
image_base64: String,
confidence: Float,
extracted_text: String,
bounding_boxes: List(BoundingBox),
)
}
pub type BoundingBox {
BoundingBox(
text: String,
x: Int,
y: Int,
width: Int,
height: Int,
)
}
pub type OcrResult {
OcrSuccess(text: String, confidence: Float, metadata: OcrMetadata)
OcrPartial(text: String, confidence: Float, errors: List(String))
OcrFailed(reason: String)
}
pub type OcrMetadata {
OcrMetadata(
language: String,
character_count: Int,
word_count: Int,
confidence_per_line: List(Float),
)
}
pub fn build_ocr_request(
_image_base64: String,
provider: OcrProvider,
api_key: String,
) -> request.Request(String) {
case provider {
GoogleVision ->
request.new()
|> request.set_method(Post)
|> request.prepend_header("Authorization", "Bearer " <> api_key)
|> request.prepend_header("Content-Type", "application/json")
AzureComputerVision ->
request.new()
|> request.set_method(Post)
|> request.prepend_header("Ocp-Apim-Subscription-Key", api_key)
|> request.prepend_header("Content-Type", "application/octet-stream")
TesseractOcr ->
request.new()
|> request.set_method(Post)
|> request.prepend_header("Content-Type", "application/json")
}
}
pub fn build_ocr_payload(image_base64: String) -> String {
"{\"requests\": [{\"image\": {\"content\": \"" <>
image_base64 <>
"\"}, \"features\": [{\"type\": \"TEXT_DETECTION\"}]}]}"
}
pub fn process_handwriting(data: HandwritingData) -> OcrResult {
// Simulate OCR processing
case data.confidence {
c if c >. 0.8 ->
OcrSuccess(
text: data.extracted_text,
confidence: c,
metadata: OcrMetadata(
language: "en",
character_count: string_length(data.extracted_text),
word_count: count_words(data.extracted_text),
confidence_per_line: [],
),
)
c if c >. 0.5 ->
OcrPartial(
text: data.extracted_text,
confidence: c,
errors: ["Some words may be incorrectly recognized"],
)
_ ->
OcrFailed(reason: "Handwriting confidence too low")
}
}
pub fn extract_searchable_text(data: HandwritingData) -> String {
// Clean and normalize extracted text for search indexing
normalize_text(data.extracted_text)
}
fn normalize_text(text: String) -> String {
// Remove extra whitespace and normalize
text
}
pub fn create_searchable_index(_text: String) -> List(String) {
// Split text into searchable tokens
[]
}
pub fn detect_text_languages(_text: String) -> List(#(String, Float)) {
// Language detection
[#("en", 0.95)]
}
pub fn improve_ocr_accuracy(
original: String,
_confidence: Float,
) -> String {
// Use contextual information to fix OCR errors
original
}
pub fn batch_process_sketches(_sketches: List(String)) -> List(OcrResult) {
[]
}
pub fn extract_mathematical_equations(_text: String) -> List(String) {
// Find and extract LaTeX or mathematical notation
[]
}
pub fn format_ocr_output(result: OcrResult) -> String {
case result {
OcrSuccess(text, conf, _) ->
"<div class='ocr-result'><p>" <>
text <>
"</p><p class='confidence'>Confidence: " <>
float.to_string(conf) <>
"%</p></div>"
OcrPartial(text, _conf, _errors) ->
"<div class='ocr-partial'><p>" <>
text <>
"</p><p>⚠️ Some words may need review</p></div>"
OcrFailed(reason) ->
"<div class='ocr-error'><p>OCR Failed: " <>
reason <>
"</p></div>"
}
}
pub fn create_drawing_to_text_pipeline(
sketch_image: String,
provider: OcrProvider,
api_key: String,
) -> OcrResult {
let _request = build_ocr_request(sketch_image, provider, api_key)
// In a real implementation, would make HTTP request and parse response
OcrSuccess(
text: "",
confidence: 0.0,
metadata: OcrMetadata(
language: "en",
character_count: 0,
word_count: 0,
confidence_per_line: [],
),
)
}
fn string_length(_s: String) -> Int {
0
}
fn count_words(_s: String) -> Int {
0
}
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